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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/08 17:53:35 UTC
[GitHub] indhub closed pull request #13068: Updates to several examples
indhub closed pull request #13068: Updates to several examples
URL: https://github.com/apache/incubator-mxnet/pull/13068
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diff --git a/example/reinforcement-learning/ddpg/README.md b/example/reinforcement-learning/ddpg/README.md
index 37f42a8292c..2e299dd5daa 100644
--- a/example/reinforcement-learning/ddpg/README.md
+++ b/example/reinforcement-learning/ddpg/README.md
@@ -1,6 +1,8 @@
# mx-DDPG
MXNet Implementation of DDPG
+## /!\ This example depends on RLLAB which is deprecated /!\
+
# Introduction
This is the MXNet implementation of [DDPG](https://arxiv.org/abs/1509.02971). It is tested in the rllab cart pole environment against rllab's native implementation and achieves comparably similar results. You can substitute with this anywhere you use rllab's DDPG with minor modifications.
diff --git a/example/reinforcement-learning/dqn/setup.sh b/example/reinforcement-learning/dqn/setup.sh
index 3069fef62ec..012ff8fb1c0 100755
--- a/example/reinforcement-learning/dqn/setup.sh
+++ b/example/reinforcement-learning/dqn/setup.sh
@@ -26,11 +26,11 @@ pip install pygame
# Install arcade learning environment
if [[ "$OSTYPE" == "linux-gnu" ]]; then
- sudo apt-get install libsdl1.2-dev libsdl-gfx1.2-dev libsdl-image1.2-dev cmake
+ sudo apt-get install libsdl1.2-dev libsdl-gfx1.2-dev libsdl-image1.2-dev cmake ninja-build
elif [[ "$OSTYPE" == "darwin"* ]]; then
brew install sdl sdl_image sdl_mixer sdl_ttf portmidi
fi
-git clone git@github.com:mgbellemare/Arcade-Learning-Environment.git || true
+git clone https://github.com/mgbellemare/Arcade-Learning-Environment || true
pushd .
cd Arcade-Learning-Environment
mkdir -p build
@@ -43,6 +43,6 @@ popd
cp Arcade-Learning-Environment/ale.cfg .
# Copy roms
-git clone git@github.com:npow/atari.git || true
+git clone https://github.com/npow/atari || true
cp -R atari/roms .
diff --git a/example/restricted-boltzmann-machine/README.md b/example/restricted-boltzmann-machine/README.md
index 129120ba996..a8769a51e05 100644
--- a/example/restricted-boltzmann-machine/README.md
+++ b/example/restricted-boltzmann-machine/README.md
@@ -8,6 +8,58 @@ Here are some samples generated by the RBM with the default hyperparameters. The
<p style="text-align:center"><img src="samples.png"/></p>
+Usage:
+
+```
+python binary_rbm_gluon.py --help
+usage: binary_rbm_gluon.py [-h] [--num-hidden NUM_HIDDEN] [--k K]
+ [--batch-size BATCH_SIZE] [--num-epoch NUM_EPOCH]
+ [--learning-rate LEARNING_RATE]
+ [--momentum MOMENTUM]
+ [--ais-batch-size AIS_BATCH_SIZE]
+ [--ais-num-batch AIS_NUM_BATCH]
+ [--ais-intermediate-steps AIS_INTERMEDIATE_STEPS]
+ [--ais-burn-in-steps AIS_BURN_IN_STEPS] [--cuda]
+ [--no-cuda] [--device-id DEVICE_ID]
+ [--data-loader-num-worker DATA_LOADER_NUM_WORKER]
+
+Restricted Boltzmann machine learning MNIST
+
+optional arguments:
+ -h, --help show this help message and exit
+ --num-hidden NUM_HIDDEN
+ number of hidden units
+ --k K number of Gibbs sampling steps used in the PCD
+ algorithm
+ --batch-size BATCH_SIZE
+ batch size
+ --num-epoch NUM_EPOCH
+ number of epochs
+ --learning-rate LEARNING_RATE
+ learning rate for stochastic gradient descent
+ --momentum MOMENTUM momentum for the stochastic gradient descent
+ --ais-batch-size AIS_BATCH_SIZE
+ batch size for AIS to estimate the log-likelihood
+ --ais-num-batch AIS_NUM_BATCH
+ number of batches for AIS to estimate the log-
+ likelihood
+ --ais-intermediate-steps AIS_INTERMEDIATE_STEPS
+ number of intermediate distributions for AIS to
+ estimate the log-likelihood
+ --ais-burn-in-steps AIS_BURN_IN_STEPS
+ number of burn in steps for each intermediate
+ distributions of AIS to estimate the log-likelihood
+ --cuda train on GPU with CUDA
+ --no-cuda train on CPU
+ --device-id DEVICE_ID
+ GPU device id
+ --data-loader-num-worker DATA_LOADER_NUM_WORKER
+ number of multithreading workers for the data loader
+```
+Default:
+```
+Namespace(ais_batch_size=100, ais_burn_in_steps=10, ais_intermediate_steps=10, ais_num_batch=10, batch_size=80, cuda=True, data_loader_num_worker=4, device_id=0, k=30, learning_rate=0.1, momentum=0.3, num_epoch=130, num_hidden=500)
+```
[1] G E Hinton & R R Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks Science **313**, 5786 (2006)<br/>
[2] R M Neal, Annealed importance sampling. Stat Comput **11** 2 (2001)<br/>
[3] R Salakhutdinov & I Murray, On the quantitative analysis of deep belief networks. In Proc. ICML '08 **25** (2008)
\ No newline at end of file
diff --git a/example/rnn-time-major/bucket_io.py b/example/rnn-time-major/bucket_io.py
deleted file mode 100644
index e689ff11267..00000000000
--- a/example/rnn-time-major/bucket_io.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-# pylint: disable=C0111,too-many-arguments,too-many-instance-attributes,too-many-locals,redefined-outer-name,fixme
-# pylint: disable=superfluous-parens, no-member, invalid-name
-
-from __future__ import print_function
-import numpy as np
-import mxnet as mx
-
-# The interface of a data iter that works for bucketing
-#
-# DataIter
-# - default_bucket_key: the bucket key for the default symbol.
-#
-# DataBatch
-# - provide_data: same as DataIter, but specific to this batch
-# - provide_label: same as DataIter, but specific to this batch
-# - bucket_key: the key for the bucket that should be used for this batch
-
-def default_read_content(path):
- with open(path) as ins:
- content = ins.read()
- content = content.replace('\n', ' <eos> ').replace('. ', ' <eos> ')
- return content
-
-def default_build_vocab(path):
- content = default_read_content(path)
- content = content.split(' ')
- idx = 1 # 0 is left for zero-padding
- the_vocab = {}
- the_vocab[' '] = 0 # put a dummy element here so that len(vocab) is correct
- for word in content:
- if len(word) == 0:
- continue
- if not word in the_vocab:
- the_vocab[word] = idx
- idx += 1
- return the_vocab
-
-def default_text2id(sentence, the_vocab):
- words = sentence.split(' ')
- words = [the_vocab[w] for w in words if len(w) > 0]
- return words
-
-def default_gen_buckets(sentences, batch_size, the_vocab):
- len_dict = {}
- max_len = -1
- for sentence in sentences:
- words = default_text2id(sentence, the_vocab)
- if len(words) == 0:
- continue
- if len(words) > max_len:
- max_len = len(words)
- if len(words) in len_dict:
- len_dict[len(words)] += 1
- else:
- len_dict[len(words)] = 1
- print(len_dict)
-
- tl = 0
- buckets = []
- for l, n in len_dict.items(): # TODO: There are better heuristic ways to do this
- if n + tl >= batch_size:
- buckets.append(l)
- tl = 0
- else:
- tl += n
- if tl > 0:
- buckets.append(max_len)
- return buckets
-
-class SimpleBatch(object):
- def __init__(self, data_names, data, data_layouts, label_names, label, label_layouts, bucket_key):
- self.data = data
- self.label = label
- self.data_names = data_names
- self.label_names = label_names
- self.data_layouts = data_layouts
- self.label_layouts = label_layouts
- self.bucket_key = bucket_key
-
- self.pad = 0
- self.index = None # TODO: what is index?
-
- @property
- def provide_data(self):
- return [mx.io.DataDesc(n, x.shape, layout=l) for n, x, l in zip(self.data_names, self.data, self.data_layouts)]
-
- @property
- def provide_label(self):
- return [mx.io.DataDesc(n, x.shape, layout=l) for n, x, l in zip(self.label_names, self.label, self.label_layouts)]
-
-class DummyIter(mx.io.DataIter):
- "A dummy iterator that always return the same batch, used for speed testing"
- def __init__(self, real_iter):
- super(DummyIter, self).__init__()
- self.real_iter = real_iter
- self.provide_data = real_iter.provide_data
- self.provide_label = real_iter.provide_label
- self.batch_size = real_iter.batch_size
-
- for batch in real_iter:
- self.the_batch = batch
- break
-
- def __iter__(self):
- return self
-
- def next(self):
- return self.the_batch
-
-class BucketSentenceIter(mx.io.DataIter):
- def __init__(self, path, vocab, buckets, batch_size,
- init_states, data_name='data', label_name='label',
- seperate_char=' <eos> ', text2id=None, read_content=None,
- time_major=True):
- super(BucketSentenceIter, self).__init__()
-
- if text2id is None:
- self.text2id = default_text2id
- else:
- self.text2id = text2id
- if read_content is None:
- self.read_content = default_read_content
- else:
- self.read_content = read_content
- content = self.read_content(path)
- sentences = content.split(seperate_char)
-
- if len(buckets) == 0:
- buckets = default_gen_buckets(sentences, batch_size, vocab)
-
- self.vocab_size = len(vocab)
- self.data_name = data_name
- self.label_name = label_name
- self.time_major = time_major
-
- buckets.sort()
- self.buckets = buckets
- self.data = [[] for _ in buckets]
-
- # pre-allocate with the largest bucket for better memory sharing
- self.default_bucket_key = max(buckets)
-
- for sentence in sentences:
- sentence = self.text2id(sentence, vocab)
- if len(sentence) == 0:
- continue
- for i, bkt in enumerate(buckets):
- if bkt >= len(sentence):
- self.data[i].append(sentence)
- break
- # we just ignore the sentence it is longer than the maximum
- # bucket size here
-
- # convert data into ndarrays for better speed during training
- data = [np.zeros((len(x), buckets[i])) for i, x in enumerate(self.data)]
- for i_bucket in range(len(self.buckets)):
- for j in range(len(self.data[i_bucket])):
- sentence = self.data[i_bucket][j]
- data[i_bucket][j, :len(sentence)] = sentence
- self.data = data
-
- # Get the size of each bucket, so that we could sample
- # uniformly from the bucket
- bucket_sizes = [len(x) for x in self.data]
-
- print("Summary of dataset ==================")
- for bkt, size in zip(buckets, bucket_sizes):
- print("bucket of len %3d : %d samples" % (bkt, size))
-
- self.batch_size = batch_size
- self.make_data_iter_plan()
-
- self.init_states = init_states
- self.init_state_arrays = [mx.nd.zeros(x[1]) for x in init_states]
-
- if self.time_major:
- self.provide_data = [mx.io.DataDesc('data', (self.default_bucket_key, batch_size), layout='TN')] + init_states
- self.provide_label = [mx.io.DataDesc('softmax_label', (self.default_bucket_key, batch_size), layout='TN')]
- else:
- self.provide_data = [('data', (batch_size, self.default_bucket_key))] + init_states
- self.provide_label = [('softmax_label', (self.batch_size, self.default_bucket_key))]
-
- def make_data_iter_plan(self):
- "make a random data iteration plan"
- # truncate each bucket into multiple of batch-size
- bucket_n_batches = []
- for i in range(len(self.data)):
- bucket_n_batches.append(len(self.data[i]) / self.batch_size)
- self.data[i] = self.data[i][:int(bucket_n_batches[i]*self.batch_size)]
-
- bucket_plan = np.hstack([np.zeros(int(n), int)+i for i, n in enumerate(bucket_n_batches)])
- np.random.shuffle(bucket_plan)
-
- bucket_idx_all = [np.random.permutation(len(x)) for x in self.data]
-
- self.bucket_plan = bucket_plan
- self.bucket_idx_all = bucket_idx_all
- self.bucket_curr_idx = [0 for x in self.data]
-
- self.data_buffer = []
- self.label_buffer = []
- for i_bucket in range(len(self.data)):
- if self.time_major:
- data = np.zeros((self.buckets[i_bucket], self.batch_size))
- label = np.zeros((self.buckets[i_bucket], self.batch_size))
- else:
- data = np.zeros((self.batch_size, self.buckets[i_bucket]))
- label = np.zeros((self.batch_size, self.buckets[i_bucket]))
-
- self.data_buffer.append(data)
- self.label_buffer.append(label)
-
- def __iter__(self):
- for i_bucket in self.bucket_plan:
- data = self.data_buffer[i_bucket]
- i_idx = self.bucket_curr_idx[i_bucket]
- idx = self.bucket_idx_all[i_bucket][i_idx:i_idx+self.batch_size]
- self.bucket_curr_idx[i_bucket] += self.batch_size
-
- init_state_names = [x[0] for x in self.init_states]
-
- if self.time_major:
- data[:] = self.data[i_bucket][idx].T
- else:
- data[:] = self.data[i_bucket][idx]
-
- label = self.label_buffer[i_bucket]
- if self.time_major:
- label[:-1, :] = data[1:, :]
- label[-1, :] = 0
- else:
- label[:, :-1] = data[:, 1:]
- label[:, -1] = 0
-
- data_all = [mx.nd.array(data)] + self.init_state_arrays
- label_all = [mx.nd.array(label)]
- data_names = ['data'] + init_state_names
- label_names = ['softmax_label']
-
- data_batch = SimpleBatch(data_names, data_all, [x.layout for x in self.provide_data],
- label_names, label_all, [x.layout for x in self.provide_label],
- self.buckets[i_bucket])
- yield data_batch
-
-
- def reset(self):
- self.bucket_curr_idx = [0 for x in self.data]
diff --git a/example/rnn-time-major/get_sherlockholmes_data.sh b/example/rnn-time-major/get_sherlockholmes_data.sh
deleted file mode 100755
index 43c8669e003..00000000000
--- a/example/rnn-time-major/get_sherlockholmes_data.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env bash
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-echo
-echo "NOTE: To continue, you need to review the licensing of the data sets used by this script"
-echo "See https://www.gutenberg.org/wiki/Gutenberg:The_Project_Gutenberg_License for the licensing"
-read -p "Please confirm you have reviewed the licensing [Y/n]:" -n 1 -r
-echo
-
-if [ $REPLY != "Y" ]
-then
- echo "License was not reviewed, aborting script."
- exit 1
-fi
-
-RNN_DIR=$(cd `dirname $0`; pwd)
-DATA_DIR="${RNN_DIR}/data/"
-
-if [[ ! -d "${DATA_DIR}" ]]; then
- echo "${DATA_DIR} doesn't exist, will create one";
- mkdir -p ${DATA_DIR}
-fi
-
-wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/sherlockholmes/sherlockholmes.train.txt;
-wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/sherlockholmes/sherlockholmes.valid.txt;
-wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/sherlockholmes/sherlockholmes.test.txt;
-wget -P ${DATA_DIR} https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tinyshakespeare/input.txt;
diff --git a/example/rnn-time-major/readme.md b/example/rnn-time-major/readme.md
deleted file mode 100644
index b30b8410b04..00000000000
--- a/example/rnn-time-major/readme.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Time major data layout for RNN
-==============================
-
-This example demonstrates an RNN implementation with Time-major layout. This implementation shows 1.5x-2x speedups compared to Batch-major RNN.
-
-As example of Batch-major RNN is available in MXNet [RNN Bucketing example](https://github.com/apache/incubator-mxnet/tree/master/example/rnn/bucketing)
-
-## Running the example
-- Prerequisite: an instance with GPU compute resources is required to run MXNet RNN
-- Make the shell script ```get_sherlockholmes_data.sh``` executable:
- ```bash
- chmod +x get_sherlockholmes_data.sh
- ```
-- Run ```get_sherlockholmes_data.sh``` to download the sherlockholmes dataset, and follow the instructions to review the license:
- ```bash
- ./get_sherlockholmes_data.sh
- ```
- The sherlockholmes data sets will be downloaded into ./data directory, and available for the example to train on.
-- Run the example:
- ```bash
- python rnn_cell_demo.py
- ```
-
- If everything goes well, console will plot training speed and perplexity that you can compare to the batch major RNN.
diff --git a/example/rnn-time-major/rnn_cell_demo.py b/example/rnn-time-major/rnn_cell_demo.py
deleted file mode 100644
index 80b281b3bdb..00000000000
--- a/example/rnn-time-major/rnn_cell_demo.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-"""A simple demo of new RNN cell with Sherlock Holmes language model."""
-
-################################################################################
-# Speed test (time major is 1.5~2 times faster than batch major).
-#
-# -- This script (time major) -----
-# 2016-10-10 18:43:21,890 Epoch[0] Batch [50] Speed: 1717.76 samples/sec Train-Perplexity=4311.345018
-# 2016-10-10 18:43:25,959 Epoch[0] Batch [100] Speed: 1573.17 samples/sec Train-Perplexity=844.092421
-# 2016-10-10 18:43:29,807 Epoch[0] Batch [150] Speed: 1663.17 samples/sec Train-Perplexity=498.080716
-# 2016-10-10 18:43:33,871 Epoch[0] Batch [200] Speed: 1574.84 samples/sec Train-Perplexity=455.051252
-# 2016-10-10 18:43:37,720 Epoch[0] Batch [250] Speed: 1662.87 samples/sec Train-Perplexity=410.500066
-# 2016-10-10 18:43:40,766 Epoch[0] Batch [300] Speed: 2100.81 samples/sec Train-Perplexity=274.317460
-# 2016-10-10 18:43:44,571 Epoch[0] Batch [350] Speed: 1682.45 samples/sec Train-Perplexity=350.132577
-# 2016-10-10 18:43:48,377 Epoch[0] Batch [400] Speed: 1681.41 samples/sec Train-Perplexity=320.674884
-# 2016-10-10 18:43:51,253 Epoch[0] Train-Perplexity=336.210212
-# 2016-10-10 18:43:51,253 Epoch[0] Time cost=33.529
-# 2016-10-10 18:43:53,373 Epoch[0] Validation-Perplexity=282.453883
-#
-# -- ../rnn/rnn_cell_demo.py (batch major) -----
-# 2016-10-10 18:44:34,133 Epoch[0] Batch [50] Speed: 1004.50 samples/sec Train-Perplexity=4398.428571
-# 2016-10-10 18:44:39,874 Epoch[0] Batch [100] Speed: 1114.85 samples/sec Train-Perplexity=771.401960
-# 2016-10-10 18:44:45,528 Epoch[0] Batch [150] Speed: 1132.03 samples/sec Train-Perplexity=525.207444
-# 2016-10-10 18:44:51,564 Epoch[0] Batch [200] Speed: 1060.37 samples/sec Train-Perplexity=453.741140
-# 2016-10-10 18:44:57,865 Epoch[0] Batch [250] Speed: 1015.78 samples/sec Train-Perplexity=411.914237
-# 2016-10-10 18:45:04,032 Epoch[0] Batch [300] Speed: 1037.92 samples/sec Train-Perplexity=381.302188
-# 2016-10-10 18:45:10,153 Epoch[0] Batch [350] Speed: 1045.49 samples/sec Train-Perplexity=363.326871
-# 2016-10-10 18:45:16,062 Epoch[0] Batch [400] Speed: 1083.21 samples/sec Train-Perplexity=377.929014
-# 2016-10-10 18:45:19,993 Epoch[0] Train-Perplexity=294.675899
-# 2016-10-10 18:45:19,993 Epoch[0] Time cost=52.604
-# 2016-10-10 18:45:21,401 Epoch[0] Validation-Perplexity=294.345659
-################################################################################
-
-import os
-import numpy as np
-import mxnet as mx
-
-from bucket_io import BucketSentenceIter, default_build_vocab
-
-data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
-
-
-def Perplexity(label, pred):
- """ Calculates prediction perplexity
-
- Args:
- label (mx.nd.array): labels array
- pred (mx.nd.array): prediction array
-
- Returns:
- float: calculated perplexity
-
- """
-
- # collapse the time, batch dimension
- label = label.reshape((-1,))
- pred = pred.reshape((-1, pred.shape[-1]))
-
- loss = 0.
- for i in range(pred.shape[0]):
- loss += -np.log(max(1e-10, pred[i][int(label[i])]))
- return np.exp(loss / label.size)
-
-
-if __name__ == '__main__':
- batch_size = 128
- buckets = [10, 20, 30, 40, 50, 60]
- num_hidden = 200
- num_embed = 200
- num_lstm_layer = 2
-
- num_epoch = 2
- learning_rate = 0.01
- momentum = 0.0
-
- # Update count per available GPUs
- gpu_count = 1
- contexts = [mx.context.gpu(i) for i in range(gpu_count)]
-
- vocab = default_build_vocab(os.path.join(data_dir, 'sherlockholmes.train.txt'))
-
- init_h = [mx.io.DataDesc('LSTM_state', (num_lstm_layer, batch_size, num_hidden), layout='TNC')]
- init_c = [mx.io.DataDesc('LSTM_state_cell', (num_lstm_layer, batch_size, num_hidden), layout='TNC')]
- init_states = init_c + init_h
-
- data_train = BucketSentenceIter(os.path.join(data_dir, 'sherlockholmes.train.txt'),
- vocab, buckets, batch_size, init_states,
- time_major=True)
- data_val = BucketSentenceIter(os.path.join(data_dir, 'sherlockholmes.valid.txt'),
- vocab, buckets, batch_size, init_states,
- time_major=True)
-
- def sym_gen(seq_len):
- """ Generates the MXNet symbol for the RNN
-
- Args:
- seq_len (int): input sequence length
-
- Returns:
- tuple: tuple containing symbol, data_names, label_names
-
- """
- data = mx.sym.Variable('data')
- label = mx.sym.Variable('softmax_label')
- embed = mx.sym.Embedding(data=data, input_dim=len(vocab),
- output_dim=num_embed, name='embed')
-
- # TODO(tofix)
- # currently all the LSTM parameters are concatenated as
- # a huge vector, and named '<name>_parameters'. By default
- # mxnet initializer does not know how to initilize this
- # guy because its name does not ends with _weight or _bias
- # or anything familiar. Here we just use a temp workaround
- # to create a variable and name it as LSTM_bias to get
- # this demo running. Note by default bias is initialized
- # as zeros, so this is not a good scheme. But calling it
- # LSTM_weight is not good, as this is 1D vector, while
- # the initialization scheme of a weight parameter needs
- # at least two dimensions.
- rnn_params = mx.sym.Variable('LSTM_bias')
-
- # RNN cell takes input of shape (time, batch, feature)
- rnn = mx.sym.RNN(data=embed, state_size=num_hidden,
- num_layers=num_lstm_layer, mode='lstm',
- name='LSTM',
- # The following params can be omitted
- # provided we do not need to apply the
- # workarounds mentioned above
- parameters=rnn_params)
-
- # the RNN cell output is of shape (time, batch, dim)
- # if we need the states and cell states in the last time
- # step (e.g. when building encoder-decoder models), we
- # can set state_outputs=True, and the RNN cell will have
- # extra outputs: rnn['LSTM_output'], rnn['LSTM_state']
- # and for LSTM, also rnn['LSTM_state_cell']
-
- # now we collapse the time and batch dimension to do the
- # final linear logistic regression prediction
- hidden = mx.sym.Reshape(data=rnn, shape=(-1, num_hidden))
-
- pred = mx.sym.FullyConnected(data=hidden, num_hidden=len(vocab),
- name='pred')
-
- # reshape to be of compatible shape as labels
- pred_tm = mx.sym.Reshape(data=pred, shape=(seq_len, -1, len(vocab)))
-
- sm = mx.sym.SoftmaxOutput(data=pred_tm, label=label, preserve_shape=True,
- name='softmax')
-
- data_names = ['data', 'LSTM_state', 'LSTM_state_cell']
- label_names = ['softmax_label']
-
- return sm, data_names, label_names
-
- if len(buckets) == 1:
- mod = mx.mod.Module(*sym_gen(buckets[0]), context=contexts)
- else:
- mod = mx.mod.BucketingModule(sym_gen,
- default_bucket_key=data_train.default_bucket_key,
- context=contexts)
-
- import logging
- head = '%(asctime)-15s %(message)s'
- logging.basicConfig(level=logging.DEBUG, format=head)
-
- mod.fit(data_train, eval_data=data_val, num_epoch=num_epoch,
- eval_metric=mx.metric.np(Perplexity),
- batch_end_callback=mx.callback.Speedometer(batch_size, 50),
- initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
- optimizer='sgd',
- optimizer_params={'learning_rate': learning_rate,
- 'momentum': momentum, 'wd': 0.00001})
diff --git a/example/rnn/README.md b/example/rnn/README.md
index a0846fa3da8..1d1df6ed768 100644
--- a/example/rnn/README.md
+++ b/example/rnn/README.md
@@ -1,6 +1,11 @@
Recurrent Neural Network Examples
===========
+For more current implementations of NLP and RNN models with MXNet, please visit [gluon-nlp](http://gluon-nlp.mxnet.io/index.html)
+
+------
+
+
This directory contains functions for creating recurrent neural networks
models using high level mxnet.rnn interface.
diff --git a/example/rnn/bucketing/README.md b/example/rnn/bucketing/README.md
index 9bbeefd21e4..7b7883d79ad 100644
--- a/example/rnn/bucketing/README.md
+++ b/example/rnn/bucketing/README.md
@@ -2,6 +2,15 @@ RNN Example
===========
This folder contains RNN examples using high level mxnet.rnn interface.
+--------------
+
+## Gluon Implementation
+
+You can check this improved [Gluon implementation](http://gluon-nlp.mxnet.io/model_zoo/language_model/index.html#word-language-model) in gluon-nlp, the largest LSTM model reaches a perplexity of 65.62.
+
+--------------
+
+
## Data
1) Review the license for the Sherlock Holmes dataset and ensure that you agree to it. Then uncomment the lines in the 'get_sherlockholmes_data.sh' script that download the dataset.
@@ -23,11 +32,11 @@ This folder contains RNN examples using high level mxnet.rnn interface.
For Python2 (GPU support only): can take 50+ minutes on AWS-EC2-p2.16xlarge
- $ python [cudnn_lstm_bucketing.py](cudnn_lstm_bucketing.py) --gpus 0,1,2,3
+ $ python [cudnn_rnn_bucketing.py](cudnn_rnn_bucketing.py) --gpus 0,1,2,3
For Python3 (GPU support only): can take 50+ minutes on AWS-EC2-p2.16xlarge
- $ python3 [cudnn_lstm_bucketing.py](cudnn_lstm_bucketing.py) --gpus 0,1,2,3
+ $ python3 [cudnn_rnn_bucketing.py](cudnn_rnn_bucketing.py) --gpus 0,1,2,3
### Performance Note:
diff --git a/example/rnn/large_word_lm/data.py b/example/rnn/large_word_lm/data.py
index b9cc3e8a89e..0ca500628d0 100644
--- a/example/rnn/large_word_lm/data.py
+++ b/example/rnn/large_word_lm/data.py
@@ -174,7 +174,7 @@ def __init__(self, data_file, vocab, batch_size, bptt):
self._iter = self._dataset.iterate_once(batch_size, bptt)
def iter_next(self):
- data = self._iter.next()
+ data = next(self._iter)
if data is None:
return False
self._next_data = mx.nd.array(data[0], dtype=np.int32)
diff --git a/example/rnn/large_word_lm/readme.md b/example/rnn/large_word_lm/readme.md
deleted file mode 100644
index 465aaa1c44b..00000000000
--- a/example/rnn/large_word_lm/readme.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Large-Scale Language Model
-This example implements the baseline model in
-[Exploring the Limits of Language Modeling](https://arxiv.org/abs/1602.02410) on the
-[Google 1-Billion Word](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) (GBW) dataset.
-
-This example reaches 48.0 test perplexity after 6 training epochs on a 1-layer, 2048-unit, 512-projection LSTM Language Model.
-It reaches 44.2 test perplexity after 35 epochs of training.
-
-The main differences with the original implementation include:
-* Synchronized gradient updates instead of asynchronized updates
-
-Each epoch for training (excluding time for evaluation on test set) takes around 80 minutes on a p3.8xlarge instance, which comes with 4 Volta V100 GPUs.
-
-# Setup dataset and build sampler
-1. Download 1-Billion Word Dataset: [Link](http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz)
-2. Download pre-processed vocabulary file which maps tokens into ids.
-3. Build sampler with cython by running `make` in the current directory. If you do not have cython installed, run `pip install cython`
-
-# Run the Script
-```
-usage: train.py [-h] [--data DATA] [--test TEST] [--vocab VOCAB]
- [--emsize EMSIZE] [--nhid NHID] [--num-proj NUM_PROJ]
- [--nlayers NLAYERS] [--epochs EPOCHS]
- [--batch-size BATCH_SIZE] [--dropout DROPOUT] [--eps EPS]
- [--bptt BPTT] [--k K] [--gpus GPUS]
- [--log-interval LOG_INTERVAL] [--seed SEED]
- [--checkpoint-dir CHECKPOINT_DIR] [--lr LR] [--clip CLIP]
- [--rescale-embed RESCALE_EMBED]
-
-Language Model on GBW
-
-optional arguments:
- -h, --help show this help message and exit
- --data DATA location of the training data
- --test TEST location of the test data
- --vocab VOCAB location of the corpus vocabulary file
- --emsize EMSIZE size of word embeddings
- --nhid NHID number of hidden units per layer
- --num-proj NUM_PROJ number of projection units per layer
- --nlayers NLAYERS number of LSTM layers
- --epochs EPOCHS number of epoch for training
- --batch-size BATCH_SIZE
- batch size per gpu
- --dropout DROPOUT dropout applied to layers (0 = no dropout)
- --eps EPS epsilon for adagrad
- --bptt BPTT sequence length
- --k K number of noise samples for estimation
- --gpus GPUS list of gpus to run, e.g. 0 or 0,2,5. empty means
- using gpu(0).
- --log-interval LOG_INTERVAL
- report interval
- --seed SEED random seed
- --checkpoint-dir CHECKPOINT_DIR
- dir for checkpoint
- --lr LR initial learning rate
- --clip CLIP gradient clipping by global norm.
- --rescale-embed RESCALE_EMBED
- scale factor for the gradients of the embedding layer
-```
-
-To reproduce the result, run
-```
-train.py --gpus=0,1,2,3 --clip=10 --lr=0.2 --dropout=0.1 --eps=1 --rescale-embed=256
---test=/path/to/heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050
---data=/path/to/training-monolingual.tokenized.shuffled/*
-```
diff --git a/example/rnn/word_lm/README.md b/example/rnn/word_lm/README.md
index beed6fc8d89..ab0a8d704b9 100644
--- a/example/rnn/word_lm/README.md
+++ b/example/rnn/word_lm/README.md
@@ -16,7 +16,7 @@ The Sherlock Holmes data is a copyright free copy of Sherlock Holmes from[(Proje
Example runs and the results:
```
-python train.py --tied --nhid 650 --emsize 650 --dropout 0.5 # Test ppl of 75.4
+python train.py --tied --nhid 650 --emsize 650 --dropout 0.5 # Test ppl of 44.26
```
```
diff --git a/example/sparse/linear_classification/data.py b/example/sparse/linear_classification/data.py
index 02984734fb9..bc5619a4bfb 100644
--- a/example/sparse/linear_classification/data.py
+++ b/example/sparse/linear_classification/data.py
@@ -24,10 +24,9 @@ def get_avazu_data(data_dir, data_name, url):
os.mkdir(data_dir)
os.chdir(data_dir)
if (not os.path.exists(data_name)):
- print("Dataset " + data_name + " not present. Downloading now ...")
- import urllib
+ print("Dataset " + data_name + " not present. Downloading now ...")
zippath = os.path.join(data_dir, data_name + ".bz2")
- urllib.urlretrieve(url + data_name + ".bz2", zippath)
+ mx.test_utils.download(url + data_name + ".bz2", zippath)
os.system("bzip2 -d %r" % data_name + ".bz2")
print("Dataset " + data_name + " is now present.")
os.chdir("..")
diff --git a/example/speech_recognition/README.md b/example/speech_recognition/README.md
index 74bad43a4fb..6f01911e130 100644
--- a/example/speech_recognition/README.md
+++ b/example/speech_recognition/README.md
@@ -28,7 +28,9 @@ With rich functionalities and convenience explained above, you can build your ow
<pre>
<code>pip install soundfile</code>
</pre>
-- Warp CTC: Follow [this instruction](https://github.com/baidu-research/warp-ctc) to compile Baidu's Warp CTC.
+- Warp CTC: Follow [this instruction](https://github.com/baidu-research/warp-ctc) to compile Baidu's Warp CTC. (Note: If you are using V100, make sure to use this [fix](https://github.com/baidu-research/warp-ctc/pull/118))
+- You need to compile MXNet with WarpCTC, follow the instructions [here](https://github.com/apache/incubator-mxnet/tree/master/example/ctc)
+- You might need to set `LD_LIBRARY_PATH` to the right path if MXNet fails to find your `libwarpctc.so`
- **We strongly recommend that you first test a model of small networks.**
diff --git a/example/speech_recognition/label_util.py b/example/speech_recognition/label_util.py
index dab1d1ef1b4..8563736052b 100644
--- a/example/speech_recognition/label_util.py
+++ b/example/speech_recognition/label_util.py
@@ -29,7 +29,7 @@ class LabelUtil:
# dataPath
def __init__(self):
- self._log = LogUtil().getlogger()
+ self._log = LogUtil.getInstance().getlogger()
self._log.debug("LabelUtil init")
def load_unicode_set(self, unicodeFilePath):
diff --git a/example/speech_recognition/log_util.py b/example/speech_recognition/log_util.py
index e61407f5f4d..65c465811fd 100644
--- a/example/speech_recognition/log_util.py
+++ b/example/speech_recognition/log_util.py
@@ -17,48 +17,44 @@
import logging
import logging.handlers
+from singleton import Singleton
+@Singleton
+class LogUtil:
-class SingletonType(type):
- def __call__(cls, *args, **kwargs):
- try:
- return cls.__instance
- except AttributeError:
- cls.__instance = super(SingletonType, cls).__call__(*args, **kwargs)
- return cls.__instance
-
-
-class LogUtil(object):
- __metaclass__ = SingletonType
_logger = None
_filename = None
- def __init__(self, filename=None):
- self._filename = filename
-
- # logger
- self._logger = logging.getLogger('logger')
- # remove default handler
- self._logger.propagate = False
-
- stream_handler = logging.StreamHandler()
- stream_formatter = logging.Formatter('[%(levelname)8s][%(asctime)s.%(msecs)03d] %(message)s',
- datefmt='%Y/%m/%d %H:%M:%S')
- stream_handler.setFormatter(stream_formatter)
-
- if self._filename is not None:
- file_max_bytes = 10 * 1024 * 1024
-
- file_handler = logging.handlers.RotatingFileHandler(filename='./log/' + self._filename,
- maxBytes=file_max_bytes,
- backupCount=10)
- file_formatter = logging.Formatter('[%(levelname)8s][%(asctime)s.%(msecs)03d] %(message)s',
- datefmt='%Y/%m/%d %H:%M:%S')
- file_handler.setFormatter(file_formatter)
- self._logger.addHandler(file_handler)
-
- self._logger.addHandler(stream_handler)
- self._logger.setLevel(logging.DEBUG)
-
- def getlogger(self):
+ def getlogger(self, filename=None):
+ if self._logger is not None and filename is not None:
+ self._logger.warning('Filename %s ignored, logger is already instanciated with %s' % (filename, self._filename))
+ if self._logger is None:
+ self._filename = filename
+
+ # logger
+ self._logger = logging.getLogger('logger')
+ # remove default handler
+ self._logger.propagate = False
+
+ stream_handler = logging.StreamHandler()
+ stream_formatter = logging.Formatter('[%(levelname)8s][%(asctime)s.%(msecs)03d] %(message)s',
+ datefmt='%Y/%m/%d %H:%M:%S')
+ stream_handler.setFormatter(stream_formatter)
+
+ if self._filename is not None:
+ file_max_bytes = 10 * 1024 * 1024
+
+ file_handler = logging.handlers.RotatingFileHandler(filename='./log/' + self._filename,
+ maxBytes=file_max_bytes,
+ backupCount=10)
+ file_formatter = logging.Formatter('[%(levelname)8s][%(asctime)s.%(msecs)03d] %(message)s',
+ datefmt='%Y/%m/%d %H:%M:%S')
+ file_handler.setFormatter(file_formatter)
+ self._logger.addHandler(file_handler)
+
+ self._logger.addHandler(stream_handler)
+ self._logger.setLevel(logging.DEBUG)
+
+
return self._logger
+
diff --git a/example/speech_recognition/main.py b/example/speech_recognition/main.py
index e45026343de..b2ea42eca0b 100644
--- a/example/speech_recognition/main.py
+++ b/example/speech_recognition/main.py
@@ -38,6 +38,8 @@
os.environ['MXNET_ENGINE_TYPE'] = "ThreadedEnginePerDevice"
os.environ['MXNET_ENABLE_GPU_P2P'] = "0"
+logUtil = LogUtil.getInstance()
+
class WHCS:
width = 0
height = 0
@@ -91,7 +93,7 @@ def load_data(args):
max_duration = args.config.getfloat('data', 'max_duration')
language = args.config.get('data', 'language')
- log = LogUtil().getlogger()
+ log = logUtil.getlogger()
labelUtil = LabelUtil.getInstance()
if mode == "train" or mode == "load":
data_json = args.config.get('data', 'train_json')
@@ -276,7 +278,7 @@ def load_model(args, contexts, data_train):
mx.random.seed(hash(datetime.now()))
# set log file name
log_filename = args.config.get('common', 'log_filename')
- log = LogUtil(filename=log_filename).getlogger()
+ log = logUtil.getlogger(filename=log_filename)
# set parameters from data section(common)
mode = args.config.get('common', 'mode')
diff --git a/example/speech_recognition/singleton.py b/example/speech_recognition/singleton.py
index 01717e4df06..fdb20c06b14 100644
--- a/example/speech_recognition/singleton.py
+++ b/example/speech_recognition/singleton.py
@@ -18,23 +18,41 @@
import logging as log
+
class Singleton:
+ """
+ A non-thread-safe helper class to ease implementing singletons.
+ This should be used as a decorator -- not a metaclass -- to the
+ class that should be a singleton.
+
+ The decorated class can define one `__init__` function that
+ takes only the `self` argument. Also, the decorated class cannot be
+ inherited from. Other than that, there are no restrictions that apply
+ to the decorated class.
+
+ To get the singleton instance, use the `instance` method. Trying
+ to use `__call__` will result in a `TypeError` being raised.
+
+ """
+
def __init__(self, decorated):
- log.debug("Singleton Init %s" % decorated)
self._decorated = decorated
def getInstance(self):
+ """
+ Returns the singleton instance. Upon its first call, it creates a
+ new instance of the decorated class and calls its `__init__` method.
+ On all subsequent calls, the already created instance is returned.
+
+ """
try:
return self._instance
except AttributeError:
self._instance = self._decorated()
return self._instance
- def __new__(cls, *args, **kwargs):
- print("__new__")
- cls._instance = super(Singleton, cls).__new__(cls, *args, **kwargs)
- return cls._instance
-
def __call__(self):
- raise TypeError("Singletons must be accessed through 'getInstance()'")
+ raise TypeError('Singletons must be accessed through `getInstance()`.')
+ def __instancecheck__(self, inst):
+ return isinstance(inst, self._decorated)
\ No newline at end of file
diff --git a/example/speech_recognition/stt_datagenerator.py b/example/speech_recognition/stt_datagenerator.py
index 8fafa790937..e1f8f13b7ba 100644
--- a/example/speech_recognition/stt_datagenerator.py
+++ b/example/speech_recognition/stt_datagenerator.py
@@ -28,6 +28,8 @@
from stt_bi_graphemes_util import generate_bi_graphemes_label
from multiprocessing import cpu_count, Process, Manager
+logUtil = LogUtil.getInstance()
+
class DataGenerator(object):
def __init__(self, save_dir, model_name, step=10, window=20, max_freq=8000, desc_file=None):
"""
@@ -86,7 +88,7 @@ def load_metadata_from_desc_file(self, desc_file, partition='train',
max_duration (float): In seconds, the maximum duration of
utterances to train or test on
"""
- logger = LogUtil().getlogger()
+ logger = logUtil.getlogger()
logger.info('Reading description file: {} for partition: {}'
.format(desc_file, partition))
audio_paths, durations, texts = [], [], []
@@ -245,7 +247,7 @@ def sample_normalize(self, k_samples=1000, overwrite=False):
Params:
k_samples (int): Use this number of samples for estimation
"""
- log = LogUtil().getlogger()
+ log = logUtil.getlogger()
log.info("Calculating mean and std from samples")
# if k_samples is negative then it goes through total dataset
if k_samples < 0:
diff --git a/example/speech_recognition/stt_io_iter.py b/example/speech_recognition/stt_io_iter.py
index 6c9bacd1a52..216dae8ac68 100644
--- a/example/speech_recognition/stt_io_iter.py
+++ b/example/speech_recognition/stt_io_iter.py
@@ -86,7 +86,7 @@ def __init__(self, count, datagen, batch_size, num_label, init_states, seq_lengt
audio_paths = audio_paths
texts = texts
- self.trainDataList = zip(durations, audio_paths, texts)
+ self.trainDataList = list(zip(durations, audio_paths, texts))
# to shuffle data
if not sort_by_duration:
random.shuffle(self.trainDataList)
@@ -103,11 +103,11 @@ def __iter__(self):
texts = []
for i in range(self.batch_size):
try:
- duration, audio_path, text = self.trainDataIter.next()
+ duration, audio_path, text = next(self.trainDataIter)
except:
random.shuffle(self.trainDataList)
self.trainDataIter = iter(self.trainDataList)
- duration, audio_path, text = self.trainDataIter.next()
+ duration, audio_path, text = next(self.trainDataIter)
audio_paths.append(audio_path)
texts.append(text)
if self.is_first_epoch:
diff --git a/example/speech_recognition/stt_metric.py b/example/speech_recognition/stt_metric.py
index ec74fc063dc..26609627ea5 100644
--- a/example/speech_recognition/stt_metric.py
+++ b/example/speech_recognition/stt_metric.py
@@ -51,7 +51,7 @@ def __init__(self, batch_size, num_gpu, is_epoch_end=False, is_logging=True):
def update(self, labels, preds):
check_label_shapes(labels, preds)
if self.is_logging:
- log = LogUtil().getlogger()
+ log = LogUtil.getInstance().getlogger()
labelUtil = LabelUtil.getInstance()
self.batch_loss = 0.
diff --git a/example/speech_recognition/stt_utils.py b/example/speech_recognition/stt_utils.py
index 0539d59f37a..cc024722331 100644
--- a/example/speech_recognition/stt_utils.py
+++ b/example/speech_recognition/stt_utils.py
@@ -15,16 +15,12 @@
# specific language governing permissions and limitations
# under the License.
-import logging
import os
import os.path
import numpy as np
-import soundfile
from numpy.lib.stride_tricks import as_strided
-
-
-logger = logging.getLogger(__name__)
+import soundfile
def calc_feat_dim(window, max_freq):
diff --git a/example/speech_recognition/train.py b/example/speech_recognition/train.py
index b1ae50b0755..e585bfd05e6 100644
--- a/example/speech_recognition/train.py
+++ b/example/speech_recognition/train.py
@@ -51,7 +51,7 @@ def do_training(args, module, data_train, data_val, begin_epoch=0):
from distutils.dir_util import mkpath
from log_util import LogUtil
- log = LogUtil().getlogger()
+ log = LogUtil.getInstance().getlogger()
mkpath(os.path.dirname(get_checkpoint_path(args)))
#seq_len = args.config.get('arch', 'max_t_count')
diff --git a/example/ssd/README.md b/example/ssd/README.md
index cc034689c7b..ec6b4b5c31a 100644
--- a/example/ssd/README.md
+++ b/example/ssd/README.md
@@ -4,6 +4,14 @@ SSD is an unified framework for object detection with a single network.
You can use the code to train/evaluate/test for object detection task.
+-------------------
+
+## Gluon Implementation
+
+You can find a Gluon implementation on [gluon-cv](https://gluon-cv.mxnet.io/build/examples_detection/train_ssd_voc.html).
+
+-------------------
+
### Disclaimer
This is a re-implementation of original SSD which is based on caffe. The official
repository is available [here](https://github.com/weiliu89/caffe/tree/ssd).
diff --git a/example/stochastic-depth/sd_cifar10.py b/example/stochastic-depth/sd_cifar10.py
index 7eb32028701..e995ea44f76 100644
--- a/example/stochastic-depth/sd_cifar10.py
+++ b/example/stochastic-depth/sd_cifar10.py
@@ -78,9 +78,6 @@
import mxnet as mx
import logging
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from utils import get_data
-
import sd_module
def residual_module(death_rate, n_channel, name_scope, context, stride=1, bn_momentum=0.9):
@@ -199,13 +196,31 @@ def get_death_rate(i_res_block):
batch_end_callbacks = [mx.callback.Speedometer(batch_size, 50)]
epoch_end_callbacks = [mx.callback.do_checkpoint('sd-%d' % (n_residual_blocks * 6 + 2))]
-
-args = type('', (), {})()
-args.batch_size = batch_size
-args.data_dir = os.path.join(os.path.dirname(__file__), "data")
+data_dir = os.path.join(os.path.dirname(__file__), "data", "cifar")
kv = mx.kvstore.create(kv_store)
-train, val = get_data.get_cifar10_iterator(args, kv)
+mx.test_utils.get_cifar10()
+
+data_shape = (3, 28, 28)
+train = mx.io.ImageRecordIter(
+ path_imgrec = os.path.join(data_dir, "train.rec"),
+ mean_img = os.path.join(data_dir, "mean.bin"),
+ data_shape = data_shape,
+ batch_size = batch_size,
+ rand_crop = True,
+ rand_mirror = True,
+ num_parts = kv.num_workers,
+ part_index = kv.rank)
+
+val = mx.io.ImageRecordIter(
+ path_imgrec = os.path.join(data_dir, "test.rec"),
+ mean_img = os.path.join(data_dir, "mean.bin"),
+ rand_crop = False,
+ rand_mirror = False,
+ data_shape = data_shape,
+ batch_size = batch_size,
+ num_parts = kv.num_workers,
+ part_index = kv.rank)
logging.basicConfig(level=logging.DEBUG)
mod_seq.fit(train, val,
diff --git a/example/stochastic-depth/sd_mnist.py b/example/stochastic-depth/sd_mnist.py
index 7eb93741ff5..6c95a23bf23 100644
--- a/example/stochastic-depth/sd_mnist.py
+++ b/example/stochastic-depth/sd_mnist.py
@@ -25,9 +25,6 @@
import mxnet as mx
import logging
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from utils import get_data
-
import sd_module
def get_conv(
@@ -121,8 +118,7 @@ def get_conv(
batch_size = 100
basedir = os.path.dirname(__file__)
-get_data.get_mnist(os.path.join(basedir, "data"))
-
+mx.test_utils.get_mnist_ubyte()
train = mx.io.MNISTIter(
image=os.path.join(basedir, "data", "train-images-idx3-ubyte"),
label=os.path.join(basedir, "data", "train-labels-idx1-ubyte"),
diff --git a/example/svm_mnist/svm_mnist.py b/example/svm_mnist/svm_mnist.py
index 679540198d2..3fc0362f6b0 100644
--- a/example/svm_mnist/svm_mnist.py
+++ b/example/svm_mnist/svm_mnist.py
@@ -20,16 +20,23 @@
## Please read the README.md document for better reference ##
#############################################################
from __future__ import print_function
+
+import logging
+import random
+
import mxnet as mx
import numpy as np
from sklearn.datasets import fetch_mldata
from sklearn.decomposition import PCA
-# import matplotlib.pyplot as plt
-import logging
+
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
+np.random.seed(1234) # set seed for deterministic ordering
+mx.random.seed(1234)
+random.seed(1234)
+
# Network declaration as symbols. The following pattern was based
# on the article, but feel free to play with the number of nodes
# and with the activation function
@@ -41,60 +48,77 @@
fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
# Here we add the ultimate layer based on L2-SVM objective
-mlp = mx.symbol.SVMOutput(data=fc3, name='svm')
+mlp_svm_l2 = mx.symbol.SVMOutput(data=fc3, name='svm_l2')
+
+# With L1-SVM objective
+mlp_svm_l1 = mx.symbol.SVMOutput(data=fc3, name='svm_l1', use_linear=True)
-# To use L1-SVM objective, comment the line above and uncomment the line below
-# mlp = mx.symbol.SVMOutput(data=fc3, name='svm', use_linear=True)
+# Compare with softmax cross entropy loss
+mlp_softmax = mx.symbol.SoftmaxOutput(data=fc3, name='softmax')
+
+print("Preparing data...")
+mnist_data = mx.test_utils.get_mnist()
+X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']])
+Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']])
+X = X.reshape((X.shape[0], -1)).astype(np.float32) * 255
# Now we fetch MNIST dataset, add some noise, as the article suggests,
# permutate and assign the examples to be used on our network
-mnist = fetch_mldata('MNIST original')
-mnist_pca = PCA(n_components=70).fit_transform(mnist.data)
+mnist_pca = PCA(n_components=70).fit_transform(X)
noise = np.random.normal(size=mnist_pca.shape)
mnist_pca += noise
-np.random.seed(1234) # set seed for deterministic ordering
p = np.random.permutation(mnist_pca.shape[0])
-X = mnist_pca[p]
-Y = mnist.target[p]
-X_show = mnist.data[p]
+X = mnist_pca[p] / 255.
+Y = Y[p]
+X_show = X[p]
# This is just to normalize the input and separate train set and test set
-X = X.astype(np.float32)/255
X_train = X[:60000]
X_test = X[60000:]
X_show = X_show[60000:]
Y_train = Y[:60000]
Y_test = Y[60000:]
-
+print("Data prepared.")
# Article's suggestion on batch size
batch_size = 200
-train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size, label_name='svm_label')
-test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=batch_size, label_name='svm_label')
-
-# Here we instatiate and fit the model for our data
-# The article actually suggests using 400 epochs,
-# But I reduced to 10, for convinience
-mod = mx.mod.Module(
- context = mx.cpu(0), # Run on CPU 0
- symbol = mlp, # Use the network we just defined
- label_names = ['svm_label'],
-)
-mod.fit(
- train_data=train_iter,
- eval_data=test_iter, # Testing data set. MXNet computes scores on test set every epoch
- batch_end_callback = mx.callback.Speedometer(batch_size, 200), # Logging module to print out progress
- num_epoch = 10, # Train for 10 epochs
- optimizer_params = {
- 'learning_rate': 0.1, # Learning rate
- 'momentum': 0.9, # Momentum for SGD with momentum
- 'wd': 0.00001, # Weight decay for regularization
- },
-)
-
-# Uncomment to view an example
-# plt.imshow((X_show[0].reshape((28,28))*255).astype(np.uint8), cmap='Greys_r')
-# plt.show()
-# print 'Result:', model.predict(X_test[0:1])[0].argmax()
-
-# Now it prints how good did the network did for this configuration
-print('Accuracy:', mod.score(test_iter, mx.metric.Accuracy())[0][1]*100, '%')
+
+ctx = mx.gpu() if len(mx.test_utils.list_gpus()) > 0 else mx.cpu()
+
+results = {}
+for output in [mlp_svm_l2, mlp_svm_l1, mlp_softmax]:
+
+ print("\nTesting with %s \n" % output.name)
+
+ label = output.name + "_label"
+
+ train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size, label_name=label)
+ test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=batch_size, label_name=label)
+
+ # Here we instatiate and fit the model for our data
+ # The article actually suggests using 400 epochs,
+ # But I reduced to 10, for convenience
+
+ mod = mx.mod.Module(
+ context = ctx,
+ symbol = output, # Use the network we just defined
+ label_names = [label],
+ )
+ mod.fit(
+ train_data=train_iter,
+ eval_data=test_iter, # Testing data set. MXNet computes scores on test set every epoch
+ batch_end_callback = mx.callback.Speedometer(batch_size, 200), # Logging module to print out progress
+ num_epoch = 10, # Train for 10 epochs
+ optimizer_params = {
+ 'learning_rate': 0.1, # Learning rate
+ 'momentum': 0.9, # Momentum for SGD with momentum
+ 'wd': 0.00001, # Weight decay for regularization
+ })
+ results[output.name] = mod.score(test_iter, mx.metric.Accuracy())[0][1]*100
+ print('Accuracy for %s:'%output.name, mod.score(test_iter, mx.metric.Accuracy())[0][1]*100, '%\n')
+
+for key, value in results.items():
+ print(key, value, "%s")
+
+#svm_l2 97.85 %s
+#svm_l1 98.15 %s
+#softmax 97.69 %s
\ No newline at end of file
diff --git a/example/svrg_module/README.md b/example/svrg_module/README.md
index 63e7ba2f2bf..250995a5715 100644
--- a/example/svrg_module/README.md
+++ b/example/svrg_module/README.md
@@ -1,7 +1,9 @@
## SVRGModule Example
+
SVRGModule is an extension to the Module API that implements SVRG optimization, which stands for Stochastic
Variance Reduced Gradient. SVRG is an optimization technique that complements SGD and has several key
properties:
+
* Employs explicit variance reduction by using a different update rule compared to SGD.
* Ability to use relatively large learning rate, which leads to faster convergence compared to SGD.
* Guarantees for fast convergence for smooth and strongly convex functions.
@@ -18,7 +20,9 @@ training script.
##### Dataset
YearPredictionMSD: contains predictions of the release year of a song from audio features. It has over
-400,000 samples with 90 features. Please uncomment data downloading script from data_reader.py to download the data.
+400,000 samples with 90 features. It will be automatically downloaded on first execution and cached.
+
+YearPredictionMSD dataset: https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd
#### Benchmarks:
An initial set of benchmarks has been performed on YearPredictionDatasetMSD with linear regression model. A jupyter
diff --git a/example/svrg_module/benchmarks/svrg_benchmark.ipynb b/example/svrg_module/benchmarks/svrg_benchmark.ipynb
index db02938af46..54ae81281db 100644
--- a/example/svrg_module/benchmarks/svrg_benchmark.ipynb
+++ b/example/svrg_module/benchmarks/svrg_benchmark.ipynb
@@ -16,17 +16,28 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
- "import mxnet as mx\n",
- "from sklearn.datasets import load_svmlight_file\n",
- "import numpy as np\n",
+ "import os\n",
"import json\n",
+ "import sys\n",
"import tempfile\n",
- "import os\n",
- "from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule\n"
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.patches as mpatches\n",
+ "import mxnet as mx\n",
+ "from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from sklearn.datasets import load_svmlight_file\n",
+ "\n",
+ "sys.path.insert(0, \"../linear_regression\")\n",
+ "from data_reader import get_year_prediction_data\n",
+ "\n",
+ "%matplotlib inline"
]
},
{
@@ -39,47 +50,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Download data file\n",
- "# from subprocess import call\n",
- "# YearPredictionMSD dataset: https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd\n",
- "# call(['wget', 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.bz2'])\n",
- "# call(['bzip2', '-d', 'YearPredictionMSD.bz2'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Extracting data...\n",
"Reading data from disk...\n"
]
}
],
"source": [
- "feature_dim = 90\n",
- "print(\"Reading data from disk...\")\n",
- "train_features, train_labels = load_svmlight_file('YearPredictionMSD', n_features=feature_dim, dtype=np.float32)\n",
- "train_features = train_features.todense()\n",
- "\n",
- "# normalize the data: subtract means and divide by standard deviations\n",
- "label_mean = train_labels.mean()\n",
- "label_std = np.sqrt(np.square(train_labels - label_mean).mean())\n",
- "feature_means = train_features.mean(axis=0)\n",
- "feature_stds = np.sqrt(np.square(train_features - feature_means).mean(axis=0))\n",
- "\n",
- "train_features = (train_features - feature_means) / feature_stds\n",
- "train_labels = (train_labels - label_mean) / label_std\n",
- "\n",
+ "feature_dim, train_features, train_labels = get_year_prediction_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"train_features = train_features[-5000:]\n",
- "train_labels = train_labels[-5000:]"
+ "train_labels = train_labels[-5000:]"
]
},
{
@@ -91,7 +85,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -119,7 +113,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -162,7 +156,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -194,19 +188,6 @@
" "
]
},
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.patches as mpatches\n",
- "import pandas as pd"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -217,7 +198,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -227,7 +208,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -236,13 +217,13 @@
"Text(0.5,0,'Epochs')"
]
},
- "execution_count": 32,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1440x864 with 1 Axes>"
]
@@ -286,7 +267,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -298,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -307,13 +288,13 @@
"Text(0.5,0,'Epochs')"
]
},
- "execution_count": 34,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1440x864 with 1 Axes>"
]
@@ -345,7 +326,7 @@
"sns.pointplot(x3, dataplot3['sgd_mse_lr_0.001'], color=color[8])\n",
"sns.pointplot(x3, dataplot3['sgd_mse_lr_0.0025'], color=color[3])\n",
"sns.pointplot(x3, dataplot3['sgd_mse_lr_0.005'], color=color[7])\n",
- "color_patch1 = mpatches.Patch(color=color[9], label=\"svrg_mse_0.025\")\n",
+ "color_patch1 = mpatches.Patch(color=color[9], label=\"svrg_mse_lr_0.025\")\n",
"color_patch2 = mpatches.Patch(color=color[8], label=\"sgd_mse_lr_0.001\")\n",
"color_patch3 = mpatches.Patch(color=color[3], label=\"sgd_mse_lr_0.0025\")\n",
"color_patch4 = mpatches.Patch(color=color[7], label=\"sgd_mse_lr_0.005\")\n",
@@ -357,21 +338,21 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 2",
+ "display_name": "Python 3",
"language": "python",
- "name": "python2"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.15"
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
}
},
"nbformat": 4,
diff --git a/example/svrg_module/linear_regression/data_reader.py b/example/svrg_module/linear_regression/data_reader.py
index d56ae03a5f4..23847d53194 100644
--- a/example/svrg_module/linear_regression/data_reader.py
+++ b/example/svrg_module/linear_regression/data_reader.py
@@ -15,21 +15,35 @@
# specific language governing permissions and limitations
# under the License.
+import bz2
+import os
+import shutil
+import mxnet as mx
import numpy as np
from sklearn.datasets import load_svmlight_file
+
# Download data file
-# from subprocess import call
# YearPredictionMSD dataset: https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd
-# call(['wget', 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.bz2'])
-# call(['bzip2', '-d', 'YearPredictionMSD.bz2'])
-def read_year_prediction_data(fileName):
+def get_year_prediction_data(dirname=None):
feature_dim = 90
+ if dirname is None:
+ dirname = os.path.join(os.path.dirname(__file__), 'data')
+ filename = 'YearPredictionMSD'
+ download_filename = os.path.join(dirname, "%s.bz2" % filename)
+ extracted_filename = os.path.join(dirname, filename)
+ if not os.path.isfile(download_filename):
+ print("Downloading data...")
+ mx.test_utils.download('https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/%s.bz2' % filename, dirname=dirname)
+ if not os.path.isfile(extracted_filename):
+ print("Extracting data...")
+ with bz2.BZ2File(download_filename) as fr, open(extracted_filename,"wb") as fw:
+ shutil.copyfileobj(fr,fw)
print("Reading data from disk...")
- train_features, train_labels = load_svmlight_file(fileName, n_features=feature_dim, dtype=np.float32)
+ train_features, train_labels = load_svmlight_file(extracted_filename, n_features=feature_dim, dtype=np.float32)
train_features = train_features.todense()
# normalize the data: subtract means and divide by standard deviations
diff --git a/example/svrg_module/linear_regression/train.py b/example/svrg_module/linear_regression/train.py
index b3d942973f1..6b6574c9618 100644
--- a/example/svrg_module/linear_regression/train.py
+++ b/example/svrg_module/linear_regression/train.py
@@ -19,7 +19,7 @@
import argparse
import mxnet as mx
from common import create_lin_reg_network, create_logger
-from data_reader import read_year_prediction_data
+from data_reader import get_year_prediction_data
parser = argparse.ArgumentParser()
parser.add_argument('-e', dest='epochs', help='number of epochs for training phase', type=int, default=100)
@@ -37,7 +37,7 @@
logger = create_logger()
kv = mx.kvstore.create(args.kv_store)
-feature_dim, train_features, train_labels = read_year_prediction_data('YearPredictionMSD')
+feature_dim, train_features, train_labels = get_year_prediction_data()
train_iter, mod = create_lin_reg_network(train_features, train_labels, feature_dim, args.batch_size, args.updateFreq,
ctx, logger)
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