You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/01/05 20:01:02 UTC
[GitHub] piiswrong closed pull request #8964: Fix example/module folder and remove duplicate examples
piiswrong closed pull request #8964: Fix example/module folder and remove duplicate examples
URL: https://github.com/apache/incubator-mxnet/pull/8964
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/example/module/README.md b/example/module/README.md
new file mode 100644
index 0000000000..99dd756ead
--- /dev/null
+++ b/example/module/README.md
@@ -0,0 +1,9 @@
+# Module Usage Example
+
+This folder contains usage examples for MXNet module.
+
+[mnist_mlp.py](https://github.com/apache/incubator-mxnet/blob/master/example/module/mnist_mlp.py): Trains a simple multilayer perceptron on the MNIST dataset
+
+[python_loss](https://github.com/apache/incubator-mxnet/blob/master/example/module/python_loss.py): Usage example for PythonLossModule
+
+[sequential_module](https://github.com/apache/incubator-mxnet/blob/master/example/module/sequential_module.py): Usage example for SequentialModule
diff --git a/example/module/lstm_bucketing.py b/example/module/lstm_bucketing.py
deleted file mode 100644
index ecc7e7be0b..0000000000
--- a/example/module/lstm_bucketing.py
+++ /dev/null
@@ -1,103 +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
-import sys
-import os
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "python")))
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "rnn")))
-import numpy as np
-import mxnet as mx
-
-from lstm import lstm_unroll
-from bucket_io import BucketSentenceIter, default_build_vocab
-
-import os.path
-data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'rnn', 'data'))
-
-def Perplexity(label, pred):
- label = label.T.reshape((-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 = 32
- buckets = [10, 20, 30, 40, 50, 60]
- #buckets = [32]
- num_hidden = 200
- num_embed = 200
- num_lstm_layer = 2
-
- #num_epoch = 25
- num_epoch = 2
- learning_rate = 0.01
- momentum = 0.0
-
- # dummy data is used to test speed without IO
- dummy_data = False
-
- contexts = [mx.context.gpu(i) for i in range(1)]
-
- vocab = default_build_vocab(os.path.join(data_dir, "ptb.train.txt"))
-
- init_c = [('l%d_init_c'%l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
- init_h = [('l%d_init_h'%l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
- init_states = init_c + init_h
-
- data_train = BucketSentenceIter(os.path.join(data_dir, "ptb.train.txt"), vocab,
- buckets, batch_size, init_states)
- data_val = BucketSentenceIter(os.path.join(data_dir, "ptb.valid.txt"), vocab,
- buckets, batch_size, init_states)
-
- if dummy_data:
- data_train = DummyIter(data_train)
- data_val = DummyIter(data_val)
-
- state_names = [x[0] for x in init_states]
- def sym_gen(seq_len):
- sym = lstm_unroll(num_lstm_layer, seq_len, len(vocab),
- num_hidden=num_hidden, num_embed=num_embed,
- num_label=len(vocab))
- data_names = ['data'] + state_names
- label_names = ['softmax_label']
- return (sym, 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':0.01, 'momentum': 0.9, 'wd': 0.00001})
-
- # Now it is very easy to use the bucketing to do scoring or collect prediction outputs
- metric = mx.metric.np(Perplexity)
- mod.score(data_val, metric)
- for name, val in metric.get_name_value():
- logging.info('Validation-%s=%f', name, val)
-
diff --git a/example/module/mnist_mlp.py b/example/module/mnist_mlp.py
index d2737dc12a..7d63a584ae 100644
--- a/example/module/mnist_mlp.py
+++ b/example/module/mnist_mlp.py
@@ -17,7 +17,6 @@
# pylint: skip-file
import os, sys
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from utils import get_data
import mxnet as mx
import numpy as np
diff --git a/example/module/python_loss.py b/example/module/python_loss.py
index 9680ac6cb0..0e15210e13 100644
--- a/example/module/python_loss.py
+++ b/example/module/python_loss.py
@@ -25,7 +25,7 @@
@numba.jit
def mc_hinge_grad(scores, labels):
scores = scores.asnumpy()
- labels = labels.asnumpy()
+ labels = labels.asnumpy().astype(int)
n, _ = scores.shape
grad = np.zeros_like(scores)
diff --git a/example/module/sequential_module.py b/example/module/sequential_module.py
index 48e1046a20..11a548d78f 100644
--- a/example/module/sequential_module.py
+++ b/example/module/sequential_module.py
@@ -17,7 +17,6 @@
# pylint: skip-file
import os, sys
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from utils import get_data
import mxnet as mx
import numpy as np
diff --git a/example/module/train_cifar10.ipynb b/example/module/train_cifar10.ipynb
deleted file mode 100644
index 4cfb973e7d..0000000000
--- a/example/module/train_cifar10.ipynb
+++ /dev/null
@@ -1,593 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train CIFAR-10 CNN model\n",
- "using MXNet's \"Module\" interface"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import mxnet\n",
- "import mxnet as mx\n",
- "import train_cifar10"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# Set up the hyper-parameters\n",
- "args = train_cifar10.command_line_args(defaults=True)\n",
- "args.gpus = \"0\"\n",
- "#args.network = \"lenet\" # Fast, not very accurate\n",
- "#args.network = \"inception-bn-28-small\" # Much more accurate & slow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " <div class=\"bk-root\">\n",
- " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
- " <span id=\"32effd9a-c11b-4a18-86b1-2c6781cf5027\">Loading BokehJS ...</span>\n",
- " </div>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/javascript": [
- "\n",
- "(function(global) {\n",
- " function now() {\n",
- " return new Date();\n",
- " }\n",
- "\n",
- " var force = \"1\";\n",
- "\n",
- " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
- " window._bokeh_onload_callbacks = [];\n",
- " window._bokeh_is_loading = undefined;\n",
- " }\n",
- "\n",
- "\n",
- " \n",
- " if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
- " window._bokeh_timeout = Date.now() + 5000;\n",
- " window._bokeh_failed_load = false;\n",
- " }\n",
- "\n",
- " var NB_LOAD_WARNING = {'data': {'text/html':\n",
- " \"<div style='background-color: #fdd'>\\n\"+\n",
- " \"<p>\\n\"+\n",
- " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
- " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
- " \"</p>\\n\"+\n",
- " \"<ul>\\n\"+\n",
- " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
- " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
- " \"</ul>\\n\"+\n",
- " \"<code>\\n\"+\n",
- " \"from bokeh.resources import INLINE\\n\"+\n",
- " \"output_notebook(resources=INLINE)\\n\"+\n",
- " \"</code>\\n\"+\n",
- " \"</div>\"}};\n",
- "\n",
- " function display_loaded() {\n",
- " if (window.Bokeh !== undefined) {\n",
- " Bokeh.$(\"#32effd9a-c11b-4a18-86b1-2c6781cf5027\").text(\"BokehJS successfully loaded.\");\n",
- " } else if (Date.now() < window._bokeh_timeout) {\n",
- " setTimeout(display_loaded, 100)\n",
- " }\n",
- " }\n",
- "\n",
- " function run_callbacks() {\n",
- " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
- " delete window._bokeh_onload_callbacks\n",
- " console.info(\"Bokeh: all callbacks have finished\");\n",
- " }\n",
- "\n",
- " function load_libs(js_urls, callback) {\n",
- " window._bokeh_onload_callbacks.push(callback);\n",
- " if (window._bokeh_is_loading > 0) {\n",
- " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
- " return null;\n",
- " }\n",
- " if (js_urls == null || js_urls.length === 0) {\n",
- " run_callbacks();\n",
- " return null;\n",
- " }\n",
- " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
- " window._bokeh_is_loading = js_urls.length;\n",
- " for (var i = 0; i < js_urls.length; i++) {\n",
- " var url = js_urls[i];\n",
- " var s = document.createElement('script');\n",
- " s.src = url;\n",
- " s.async = false;\n",
- " s.onreadystatechange = s.onload = function() {\n",
- " window._bokeh_is_loading--;\n",
- " if (window._bokeh_is_loading === 0) {\n",
- " console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
- " run_callbacks()\n",
- " }\n",
- " };\n",
- " s.onerror = function() {\n",
- " console.warn(\"failed to load library \" + url);\n",
- " };\n",
- " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
- " }\n",
- " };var element = document.getElementById(\"32effd9a-c11b-4a18-86b1-2c6781cf5027\");\n",
- " if (element == null) {\n",
- " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '32effd9a-c11b-4a18-86b1-2c6781cf5027' but no matching script tag was found. \")\n",
- " return false;\n",
- " }\n",
- "\n",
- " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.js'];\n",
- "\n",
- " var inline_js = [\n",
- " function(Bokeh) {\n",
- " Bokeh.set_log_level(\"info\");\n",
- " },\n",
- " \n",
- " function(Bokeh) {\n",
- " \n",
- " Bokeh.$(\"#32effd9a-c11b-4a18-86b1-2c6781cf5027\").text(\"BokehJS is loading...\");\n",
- " },\n",
- " function(Bokeh) {\n",
- " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.css\");\n",
- " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.css\");\n",
- " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.css\");\n",
- " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.css\");\n",
- " }\n",
- " ];\n",
- "\n",
- " function run_inline_js() {\n",
- " \n",
- " if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
- " for (var i = 0; i < inline_js.length; i++) {\n",
- " inline_js[i](window.Bokeh);\n",
- " }if (force === \"1\") {\n",
- " display_loaded();\n",
- " }} else if (Date.now() < window._bokeh_timeout) {\n",
- " setTimeout(run_inline_js, 100);\n",
- " } else if (!window._bokeh_failed_load) {\n",
- " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
- " window._bokeh_failed_load = true;\n",
- " } else if (!force) {\n",
- " var cell = $(\"#32effd9a-c11b-4a18-86b1-2c6781cf5027\").parents('.cell').data().cell;\n",
- " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
- " }\n",
- "\n",
- " }\n",
- "\n",
- " if (window._bokeh_is_loading === 0) {\n",
- " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
- " run_inline_js();\n",
- " } else {\n",
- " load_libs(js_urls, function() {\n",
- " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
- " run_inline_js();\n",
- " });\n",
- " }\n",
- "}(this));"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- " <div class=\"bk-root\">\n",
- " <div class=\"plotdiv\" id=\"8813280e-065e-435d-846c-31a252dddcc7\"></div>\n",
- " </div>\n",
- "<script type=\"text/javascript\">\n",
- " \n",
- " (function(global) {\n",
- " function now() {\n",
- " return new Date();\n",
- " }\n",
- " \n",
- " var force = \"\";\n",
- " \n",
- " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
- " window._bokeh_onload_callbacks = [];\n",
- " window._bokeh_is_loading = undefined;\n",
- " }\n",
- " \n",
- " \n",
- " \n",
- " if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
- " window._bokeh_timeout = Date.now() + 0;\n",
- " window._bokeh_failed_load = false;\n",
- " }\n",
- " \n",
- " var NB_LOAD_WARNING = {'data': {'text/html':\n",
- " \"<div style='background-color: #fdd'>\\n\"+\n",
- " \"<p>\\n\"+\n",
- " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
- " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
- " \"</p>\\n\"+\n",
- " \"<ul>\\n\"+\n",
- " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
- " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
- " \"</ul>\\n\"+\n",
- " \"<code>\\n\"+\n",
- " \"from bokeh.resources import INLINE\\n\"+\n",
- " \"output_notebook(resources=INLINE)\\n\"+\n",
- " \"</code>\\n\"+\n",
- " \"</div>\"}};\n",
- " \n",
- " function display_loaded() {\n",
- " if (window.Bokeh !== undefined) {\n",
- " Bokeh.$(\"#8813280e-065e-435d-846c-31a252dddcc7\").text(\"BokehJS successfully loaded.\");\n",
- " } else if (Date.now() < window._bokeh_timeout) {\n",
- " setTimeout(display_loaded, 100)\n",
- " }\n",
- " }if ((window.Jupyter !== undefined) && Jupyter.notebook.kernel) {\n",
- " comm_manager = Jupyter.notebook.kernel.comm_manager\n",
- " comm_manager.register_target(\"29afa51c-a944-4f51-8ffa-a03b925a1f47\", function () {});\n",
- " }\n",
- " \n",
- " function run_callbacks() {\n",
- " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
- " delete window._bokeh_onload_callbacks\n",
- " console.info(\"Bokeh: all callbacks have finished\");\n",
- " }\n",
- " \n",
- " function load_libs(js_urls, callback) {\n",
- " window._bokeh_onload_callbacks.push(callback);\n",
- " if (window._bokeh_is_loading > 0) {\n",
- " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
- " return null;\n",
- " }\n",
- " if (js_urls == null || js_urls.length === 0) {\n",
- " run_callbacks();\n",
- " return null;\n",
- " }\n",
- " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
- " window._bokeh_is_loading = js_urls.length;\n",
- " for (var i = 0; i < js_urls.length; i++) {\n",
- " var url = js_urls[i];\n",
- " var s = document.createElement('script');\n",
- " s.src = url;\n",
- " s.async = false;\n",
- " s.onreadystatechange = s.onload = function() {\n",
- " window._bokeh_is_loading--;\n",
- " if (window._bokeh_is_loading === 0) {\n",
- " console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
- " run_callbacks()\n",
- " }\n",
- " };\n",
- " s.onerror = function() {\n",
- " console.warn(\"failed to load library \" + url);\n",
- " };\n",
- " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
- " }\n",
- " };var element = document.getElementById(\"8813280e-065e-435d-846c-31a252dddcc7\");\n",
- " if (element == null) {\n",
- " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8813280e-065e-435d-846c-31a252dddcc7' but no matching script tag was found. \")\n",
- " return false;\n",
- " }\n",
- " \n",
- " var js_urls = [];\n",
- " \n",
- " var inline_js = [\n",
- " function(Bokeh) {\n",
- " Bokeh.$(function() {\n",
- " var docs_json = {\"f847dff6-ab8a-4fd1-8be0-e79b39334749\":{\"roots\":{\"references\":[{\"attributes\":{\"months\":[0,6]},\"id\":\"dd14ae70-3ab7-4fd4-8fae-0e8db050826a\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"e21f9d30-e2e0-491a-abaf-5ead1f475c72\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6aa5fde0-6aa4-434b-8f87-1bb3def62ed1\",\"type\":\"Circle\"},{\"attributes\":{\"line_color\":{\"value\":\"green\"},\"line_width\":{\"value\":2},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"4d07fb5a-33b4-404a-9379-5ab6938da677\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"21f9bad6-481d-41fb-b974-57ea3ff7f1af\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"831a2985-7d96
-4db7-a559-e2926d17a7f6\",\"type\":\"Line\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"8d48b39d-05ce-4f28-8b5d-9be03475c1d4\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"7f62ddb4-114c-4896-86c3-96750fb8def6\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"74e596f1-3572-46b8-a214-6df3913ca9fb\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"29773c7b-27c0-48ff-b0f6-d0a88692670d\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"b194d165-b586-46fa-bb52-8f0f4bd493c4\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"774141d7-0706-4a09-9db0-86453495a676\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":[],\"y\":[]}},\"id\":\"6d64ac4e-6e15-4c79-8309-b4764aca4f61\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"nu
m_minor_ticks\":0},\"id\":\"6f4ee18d-44f0-447c-96d4-4cbd2630f382\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"d030fc41-922e-4c4a-9636-c12f9e0ba354\",\"type\":\"Line\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":1.5},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"9108b55f-4dfc-4d16-a21a-5a85cc88b4c4\",\"type\":\"Circle\"},{\"attributes\":{\"plot\":null,\"text\":null},\"id\":\"f886da61-3674-459d-aaae-4baaed554f3d\",\"type\":\"Title\"},{\"attributes\":{\"num_minor_ticks\":5},\"id\":\"6d5a77bf-c5d8-40ca-b997-29e48d5dc622\",\"type\":\"DatetimeTicker\"},{\"attributes\":{\"callback\":null},\"id\":\"d2fe6612-09b0-46be-8673-4e609aafb3fb\",\"type\":\"DataRange1d\"},{\"attr
ibutes\":{\"callback\":null},\"id\":\"8a4f53ae-29b3-4b01-80f2-68f274c3bc2b\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"ea1acf82-c85c-43eb-9140-b309fa0b0017\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6632addc-9e3a-4324-8684-1c3eeef1a580\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"9108b55f-4dfc-4d16-a21a-5a85cc88b4c4\",\"type\":\"Circle\"},\"selection_glyph\":null,\"visible\":false},\"id\":\"ea4cefb9-dbd6-45d8-bab8-326a9ca2833e\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"93dfa78a-816c-488a-b683-d46589aff77c\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"axis_label\":\"Training time\",\"formatter\":{\"id\":\"72c7824a-3c1f-49c1-a876-008f33b70b68\",\"type\":\"DatetimeTickFormatter\"},\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6d5
a77bf-c5d8-40ca-b997-29e48d5dc622\",\"type\":\"DatetimeTicker\"}},\"id\":\"185f2429-2cf6-47c4-b89d-143b83f8eb6e\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"line_alpha\":{\"value\":0.3},\"line_color\":{\"value\":\"#1f77b4\"},\"line_dash\":[2,4],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"831a2985-7d96-4db7-a559-e2926d17a7f6\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"728724cd-5ad3-467e-8694-65cfd5c8892b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4d07fb5a-33b4-404a-9379-5ab6938da677\",\"type\":\"Line\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"d030fc41-922e-4c4a-9636-c12f9e0ba354\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"583cb19e-e81f-4229-a03e-300ccdad69cb\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"ba102579-4371-4d46-8efe-bb63bf5ac700\",\"type\":\"HelpTool\"},{\"attributes\":{\"months\":[0,2,4,6,8,
10]},\"id\":\"b4273502-7ddf-4316-ba03-e9d77406d017\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"9b38934f-2246-4fd5-9629-5fed9577c817\",\"type\":\"DaysTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"a89af1c1-53cf-4c7f-a03e-4efb8298b516\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"05eb39ff-3243-4ceb-b390-6b539d2277e7\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"ea5c46b7-fbb1-41ec-89ec-915d4f00a7f3\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"li
ne_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"05eb39ff-3243-4ceb-b390-6b539d2277e7\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"7eea0ddb-db8d-46ce-9fe0-96366980bfd2\",\"type\":\"ResetTool\"},{\"attributes\":{\"items\":[{\"id\":\"ef08bd27-c2f3-49ee-a09f-d729c90b644b\",\"type\":\"LegendItem\"},{\"id\":\"a9a5b513-4ef8-40df-b5c1-4edb707de048\",\"type\":\"LegendItem\"}],\"location\":\"bottom_right\",\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"30269d85-9497-46a2-b4f0-115998ba1f00\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"72c7824a-3c1f-49c1-a876-008f33b70b68\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"data_source\":{\"id\":\"6d64ac4e-6e15-4c79-8309-b4764aca4f61\",\"type\":\"ColumnDataSource\"},\"
glyph\":{\"id\":\"bcd89277-296a-4318-b2eb-0591d6520177\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"6aa5fde0-6aa4-434b-8f87-1bb3def62ed1\",\"type\":\"Circle\"},\"selection_glyph\":null},\"id\":\"809e6336-d5b0-43d3-9337-3d380d4b02a9\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"4e9b3062-36eb-4149-adad-c7a4b0ba96aa\",\"type\":\"DaysTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.3},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.3},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":1.5},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6632addc-9e3a-4324-8684-1c3eeef1a580\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":[],\"y\":[]}},\"id\":\"21f9bad6-481d-41fb-b974-57ea3ff7f1af\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_
tap\":\"auto\",\"tools\":[{\"id\":\"4217886e-0f96-45d8-be38-754aac40e061\",\"type\":\"PanTool\"},{\"id\":\"93dfa78a-816c-488a-b683-d46589aff77c\",\"type\":\"WheelZoomTool\"},{\"id\":\"ea5c46b7-fbb1-41ec-89ec-915d4f00a7f3\",\"type\":\"BoxZoomTool\"},{\"id\":\"f00d301e-eb5f-475a-999f-05da8346f3a1\",\"type\":\"SaveTool\"},{\"id\":\"7eea0ddb-db8d-46ce-9fe0-96366980bfd2\",\"type\":\"ResetTool\"},{\"id\":\"ba102579-4371-4d46-8efe-bb63bf5ac700\",\"type\":\"HelpTool\"}]},\"id\":\"ab630684-4927-4f7d-92fc-a7e63db0f9c0\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":[],\"y\":[]}},\"id\":\"728724cd-5ad3-467e-8694-65cfd5c8892b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"below\":[{\"id\":\"185f2429-2cf6-47c4-b89d-143b83f8eb6e\",\"type\":\"DatetimeAxis\"}],\"left\":[{\"id\":\"98eaea83-2f3a-4f09-a273-b5102bb1e852\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"185f2429-2cf6-47c4-b89d-143b83f8eb6e\",\"type\":\"DatetimeAxis\"},{\"
id\":\"5964d98a-e17a-438b-b745-f21169bd1fb2\",\"type\":\"Grid\"},{\"id\":\"98eaea83-2f3a-4f09-a273-b5102bb1e852\",\"type\":\"LinearAxis\"},{\"id\":\"87b53edb-380a-4245-8c59-a548946c329a\",\"type\":\"Grid\"},{\"id\":\"05eb39ff-3243-4ceb-b390-6b539d2277e7\",\"type\":\"BoxAnnotation\"},{\"id\":\"30269d85-9497-46a2-b4f0-115998ba1f00\",\"type\":\"Legend\"},{\"id\":\"7f62ddb4-114c-4896-86c3-96750fb8def6\",\"type\":\"GlyphRenderer\"},{\"id\":\"ea4cefb9-dbd6-45d8-bab8-326a9ca2833e\",\"type\":\"GlyphRenderer\"},{\"id\":\"583cb19e-e81f-4229-a03e-300ccdad69cb\",\"type\":\"GlyphRenderer\"},{\"id\":\"809e6336-d5b0-43d3-9337-3d380d4b02a9\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"f886da61-3674-459d-aaae-4baaed554f3d\",\"type\":\"Title\"},\"tool_events\":{\"id\":\"d34d14c5-5ce6-4e0a-b4a6-6dd2716c6aeb\",\"type\":\"ToolEvents\"},\"toolbar\":{\"id\":\"ab630684-4927-4f7d-92fc-a7e63db0f9c0\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"d2fe6612-09b0-46be-8673-4e609aafb3fb\",\"type\":\"DataRange1
d\"},\"y_range\":{\"id\":\"8a4f53ae-29b3-4b01-80f2-68f274c3bc2b\",\"type\":\"DataRange1d\"}},\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"1f26da1d-bc54-4d8c-a3b7-8cda2999af19\",\"type\":\"DaysTicker\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"4217886e-0f96-45d8-be38-754aac40e061\",\"type\":\"PanTool\"},{\"attributes\":{\"axis_label\":\"accuracy\",\"formatter\":{\"id\":\"29773c7b-27c0-48ff-b0f6-d0a88692670d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"74e596f1-3572-46b8-a214-6df3913ca9fb\",\"type\":\"BasicTicker\"}},\"id\":\"98eaea83-2f3a-4f09-a273-b5102bb1e852\",\"type\":\"LinearAxis\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"e4644a83-5617-4c12-92d6-d388a49e81a7\",\"type\":\"DaysTicker\"},{\"att
ributes\":{},\"id\":\"d34d14c5-5ce6-4e0a-b4a6-6dd2716c6aeb\",\"type\":\"ToolEvents\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"y\",\"x\"],\"data\":{\"x\":[],\"y\":[]}},\"id\":\"ea1acf82-c85c-43eb-9140-b309fa0b0017\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6d5a77bf-c5d8-40ca-b997-29e48d5dc622\",\"type\":\"DatetimeTicker\"}},\"id\":\"5964d98a-e17a-438b-b745-f21169bd1fb2\",\"type\":\"Grid\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"green\"},\"line_width\":{\"value\":2},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"bcd89277-296a-4318-b2eb-0591d6520177\",\"type\":\"Circle\"},{\"attributes\":{\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"f00d301e-eb5f-475a-999f-05da8346f3a1\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"cfd56
63b-aa9e-4931-9fc1-732dbe3e382f\",\"type\":\"YearsTicker\"},{\"attributes\":{\"label\":{\"value\":\"train\"},\"renderers\":[{\"id\":\"7f62ddb4-114c-4896-86c3-96750fb8def6\",\"type\":\"GlyphRenderer\"},{\"id\":\"ea4cefb9-dbd6-45d8-bab8-326a9ca2833e\",\"type\":\"GlyphRenderer\"}]},\"id\":\"ef08bd27-c2f3-49ee-a09f-d729c90b644b\",\"type\":\"LegendItem\"},{\"attributes\":{\"label\":{\"value\":\"validation\"},\"renderers\":[{\"id\":\"583cb19e-e81f-4229-a03e-300ccdad69cb\",\"type\":\"GlyphRenderer\"}]},\"id\":\"a9a5b513-4ef8-40df-b5c1-4edb707de048\",\"type\":\"LegendItem\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"74e596f1-3572-46b8-a214-6df3913ca9fb\",\"type\":\"BasicTicker\"}},\"id\":\"87b53edb-380a-4245-8c59-a548946c329a\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_dash\":[2,4],\"x\":{\"field\":\"x\"},\"y\"
:{\"field\":\"y\"}},\"id\":\"8d48b39d-05ce-4f28-8b5d-9be03475c1d4\",\"type\":\"Line\"}],\"root_ids\":[\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.3\"}};\n",
- " var render_items = [{\"docid\":\"f847dff6-ab8a-4fd1-8be0-e79b39334749\",\"elementid\":\"8813280e-065e-435d-846c-31a252dddcc7\",\"modelid\":\"01107ba2-ae75-41b0-bddc-5e93690ea7e8\",\"notebook_comms_target\":\"29afa51c-a944-4f51-8ffa-a03b925a1f47\"}];\n",
- " \n",
- " Bokeh.embed.embed_items(docs_json, render_items);\n",
- " });\n",
- " },\n",
- " function(Bokeh) {\n",
- " }\n",
- " ];\n",
- " \n",
- " function run_inline_js() {\n",
- " \n",
- " if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
- " for (var i = 0; i < inline_js.length; i++) {\n",
- " inline_js[i](window.Bokeh);\n",
- " }if (force === \"1\") {\n",
- " display_loaded();\n",
- " }} else if (Date.now() < window._bokeh_timeout) {\n",
- " setTimeout(run_inline_js, 100);\n",
- " } else if (!window._bokeh_failed_load) {\n",
- " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
- " window._bokeh_failed_load = true;\n",
- " } else if (!force) {\n",
- " var cell = $(\"#8813280e-065e-435d-846c-31a252dddcc7\").parents('.cell').data().cell;\n",
- " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
- " }\n",
- " \n",
- " }\n",
- " \n",
- " if (window._bokeh_is_loading === 0) {\n",
- " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
- " run_inline_js();\n",
- " } else {\n",
- " load_libs(js_urls, function() {\n",
- " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
- " run_inline_js();\n",
- " });\n",
- " }\n",
- " }(this));\n",
- "</script>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Configure charts to plot while training\n",
- "from mxnet.notebook.callback import LiveLearningCurve\n",
- "cb_args = LiveLearningCurve('accuracy', 5).callback_args()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2016-11-04 18:05:30,714 Node[0] start with arguments Namespace(batch_size=128, data_dir='/efs/datasets/users/leodirac/code/workplace/leodirac/mxnet/example/image-classification/cifar10/', gpus='0', kv_store='local', load_epoch=None, lr=0.05, lr_factor=1, lr_factor_epoch=1, model_prefix=None, network='inception-bn-28-small', num_epochs=20, num_examples=60000, save_model_prefix=None)\n",
- "2016-11-04 18:05:30,715 Node[0] running on ip-172-31-59-245\n",
- "2016-11-04 18:05:32,172 Node[0] Starting with devices [gpu(0)]\n",
- "2016-11-04 18:05:32,175 Node[0] start training for 20 epochs...\n",
- "2016-11-04 18:07:11,468 Node[0] Epoch[0] Train-accuracy=0.566211\n",
- "2016-11-04 18:07:11,469 Node[0] Epoch[0] Train-top_k_accuracy_5=0.948242\n",
- "2016-11-04 18:07:11,470 Node[0] Epoch[0] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:07:11,471 Node[0] Epoch[0] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:07:11,471 Node[0] Epoch[0] Time cost=98.542\n",
- "2016-11-04 18:07:17,454 Node[0] Epoch[0] Validation-accuracy=nan\n",
- "2016-11-04 18:07:17,455 Node[0] Epoch[0] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:07:17,456 Node[0] Epoch[0] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:07:17,457 Node[0] Epoch[0] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:08:57,069 Node[0] Epoch[1] Train-accuracy=0.679883\n",
- "2016-11-04 18:08:57,069 Node[0] Epoch[1] Train-top_k_accuracy_5=0.973047\n",
- "2016-11-04 18:08:57,071 Node[0] Epoch[1] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:08:57,072 Node[0] Epoch[1] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:08:57,072 Node[0] Epoch[1] Time cost=99.614\n",
- "2016-11-04 18:09:02,598 Node[0] Epoch[1] Validation-accuracy=nan\n",
- "2016-11-04 18:09:02,599 Node[0] Epoch[1] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:09:02,599 Node[0] Epoch[1] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:09:02,600 Node[0] Epoch[1] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:10:42,219 Node[0] Epoch[2] Train-accuracy=0.742388\n",
- "2016-11-04 18:10:42,221 Node[0] Epoch[2] Train-top_k_accuracy_5=0.981170\n",
- "2016-11-04 18:10:42,222 Node[0] Epoch[2] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:10:42,223 Node[0] Epoch[2] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:10:42,223 Node[0] Epoch[2] Time cost=99.622\n",
- "2016-11-04 18:10:47,755 Node[0] Epoch[2] Validation-accuracy=nan\n",
- "2016-11-04 18:10:47,756 Node[0] Epoch[2] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:10:47,757 Node[0] Epoch[2] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:10:47,758 Node[0] Epoch[2] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:12:27,771 Node[0] Epoch[3] Train-accuracy=0.775586\n",
- "2016-11-04 18:12:27,772 Node[0] Epoch[3] Train-top_k_accuracy_5=0.987891\n",
- "2016-11-04 18:12:27,773 Node[0] Epoch[3] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:12:27,774 Node[0] Epoch[3] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:12:27,775 Node[0] Epoch[3] Time cost=100.017\n",
- "2016-11-04 18:12:33,304 Node[0] Epoch[3] Validation-accuracy=nan\n",
- "2016-11-04 18:12:33,305 Node[0] Epoch[3] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:12:33,306 Node[0] Epoch[3] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:12:33,307 Node[0] Epoch[3] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:14:13,252 Node[0] Epoch[4] Train-accuracy=0.793945\n",
- "2016-11-04 18:14:13,253 Node[0] Epoch[4] Train-top_k_accuracy_5=0.991016\n",
- "2016-11-04 18:14:13,253 Node[0] Epoch[4] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:14:13,254 Node[0] Epoch[4] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:14:13,255 Node[0] Epoch[4] Time cost=99.948\n",
- "2016-11-04 18:14:18,787 Node[0] Epoch[4] Validation-accuracy=nan\n",
- "2016-11-04 18:14:18,787 Node[0] Epoch[4] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:14:18,788 Node[0] Epoch[4] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:14:18,789 Node[0] Epoch[4] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:15:58,508 Node[0] Epoch[5] Train-accuracy=0.819311\n",
- "2016-11-04 18:15:58,509 Node[0] Epoch[5] Train-top_k_accuracy_5=0.991987\n",
- "2016-11-04 18:15:58,510 Node[0] Epoch[5] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:15:58,511 Node[0] Epoch[5] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:15:58,511 Node[0] Epoch[5] Time cost=99.722\n",
- "2016-11-04 18:16:04,043 Node[0] Epoch[5] Validation-accuracy=nan\n",
- "2016-11-04 18:16:04,044 Node[0] Epoch[5] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:16:04,045 Node[0] Epoch[5] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:16:04,046 Node[0] Epoch[5] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:17:43,899 Node[0] Epoch[6] Train-accuracy=0.829688\n",
- "2016-11-04 18:17:43,900 Node[0] Epoch[6] Train-top_k_accuracy_5=0.993750\n",
- "2016-11-04 18:17:43,901 Node[0] Epoch[6] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:17:43,902 Node[0] Epoch[6] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:17:43,903 Node[0] Epoch[6] Time cost=99.856\n",
- "2016-11-04 18:17:49,441 Node[0] Epoch[6] Validation-accuracy=nan\n",
- "2016-11-04 18:17:49,441 Node[0] Epoch[6] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:17:49,442 Node[0] Epoch[6] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:17:49,443 Node[0] Epoch[6] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:19:29,163 Node[0] Epoch[7] Train-accuracy=0.844151\n",
- "2016-11-04 18:19:29,164 Node[0] Epoch[7] Train-top_k_accuracy_5=0.994992\n",
- "2016-11-04 18:19:29,165 Node[0] Epoch[7] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:19:29,166 Node[0] Epoch[7] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:19:29,167 Node[0] Epoch[7] Time cost=99.723\n",
- "2016-11-04 18:19:34,688 Node[0] Epoch[7] Validation-accuracy=nan\n",
- "2016-11-04 18:19:34,689 Node[0] Epoch[7] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:19:34,689 Node[0] Epoch[7] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:19:34,690 Node[0] Epoch[7] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:21:15,117 Node[0] Epoch[8] Train-accuracy=0.862695\n",
- "2016-11-04 18:21:15,118 Node[0] Epoch[8] Train-top_k_accuracy_5=0.995703\n",
- "2016-11-04 18:21:15,118 Node[0] Epoch[8] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:21:15,120 Node[0] Epoch[8] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:21:15,121 Node[0] Epoch[8] Time cost=100.430\n",
- "2016-11-04 18:21:21,087 Node[0] Epoch[8] Validation-accuracy=nan\n",
- "2016-11-04 18:21:21,088 Node[0] Epoch[8] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:21:21,088 Node[0] Epoch[8] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:21:21,089 Node[0] Epoch[8] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:23:00,978 Node[0] Epoch[9] Train-accuracy=0.872070\n",
- "2016-11-04 18:23:00,979 Node[0] Epoch[9] Train-top_k_accuracy_5=0.994141\n",
- "2016-11-04 18:23:00,981 Node[0] Epoch[9] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:23:00,982 Node[0] Epoch[9] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:23:00,983 Node[0] Epoch[9] Time cost=99.893\n",
- "2016-11-04 18:23:06,510 Node[0] Epoch[9] Validation-accuracy=nan\n",
- "2016-11-04 18:23:06,511 Node[0] Epoch[9] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:23:06,511 Node[0] Epoch[9] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:23:06,512 Node[0] Epoch[9] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:24:46,159 Node[0] Epoch[10] Train-accuracy=0.878606\n",
- "2016-11-04 18:24:46,160 Node[0] Epoch[10] Train-top_k_accuracy_5=0.997196\n",
- "2016-11-04 18:24:46,161 Node[0] Epoch[10] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:24:46,162 Node[0] Epoch[10] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:24:46,163 Node[0] Epoch[10] Time cost=99.646\n",
- "2016-11-04 18:24:51,684 Node[0] Epoch[10] Validation-accuracy=nan\n",
- "2016-11-04 18:24:51,685 Node[0] Epoch[10] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:24:51,685 Node[0] Epoch[10] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:24:51,686 Node[0] Epoch[10] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:26:31,614 Node[0] Epoch[11] Train-accuracy=0.889062\n",
- "2016-11-04 18:26:31,615 Node[0] Epoch[11] Train-top_k_accuracy_5=0.996094\n",
- "2016-11-04 18:26:31,616 Node[0] Epoch[11] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:26:31,617 Node[0] Epoch[11] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:26:31,618 Node[0] Epoch[11] Time cost=99.931\n",
- "2016-11-04 18:26:37,150 Node[0] Epoch[11] Validation-accuracy=nan\n",
- "2016-11-04 18:26:37,151 Node[0] Epoch[11] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:26:37,152 Node[0] Epoch[11] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:26:37,152 Node[0] Epoch[11] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:28:17,021 Node[0] Epoch[12] Train-accuracy=0.895117\n",
- "2016-11-04 18:28:17,023 Node[0] Epoch[12] Train-top_k_accuracy_5=0.997266\n",
- "2016-11-04 18:28:17,024 Node[0] Epoch[12] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:28:17,025 Node[0] Epoch[12] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:28:17,026 Node[0] Epoch[12] Time cost=99.873\n",
- "2016-11-04 18:28:22,553 Node[0] Epoch[12] Validation-accuracy=nan\n",
- "2016-11-04 18:28:22,554 Node[0] Epoch[12] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:28:22,555 Node[0] Epoch[12] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:28:22,556 Node[0] Epoch[12] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:30:02,235 Node[0] Epoch[13] Train-accuracy=0.904447\n",
- "2016-11-04 18:30:02,236 Node[0] Epoch[13] Train-top_k_accuracy_5=0.997997\n",
- "2016-11-04 18:30:02,237 Node[0] Epoch[13] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:30:02,238 Node[0] Epoch[13] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:30:02,239 Node[0] Epoch[13] Time cost=99.683\n",
- "2016-11-04 18:30:07,772 Node[0] Epoch[13] Validation-accuracy=nan\n",
- "2016-11-04 18:30:07,773 Node[0] Epoch[13] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:30:07,774 Node[0] Epoch[13] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:30:07,774 Node[0] Epoch[13] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:31:47,692 Node[0] Epoch[14] Train-accuracy=0.902734\n",
- "2016-11-04 18:31:47,693 Node[0] Epoch[14] Train-top_k_accuracy_5=0.998633\n",
- "2016-11-04 18:31:47,694 Node[0] Epoch[14] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:31:47,695 Node[0] Epoch[14] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:31:47,696 Node[0] Epoch[14] Time cost=99.921\n",
- "2016-11-04 18:31:53,226 Node[0] Epoch[14] Validation-accuracy=nan\n",
- "2016-11-04 18:31:53,227 Node[0] Epoch[14] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:31:53,228 Node[0] Epoch[14] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:31:53,228 Node[0] Epoch[14] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:33:32,868 Node[0] Epoch[15] Train-accuracy=0.919471\n",
- "2016-11-04 18:33:32,869 Node[0] Epoch[15] Train-top_k_accuracy_5=0.998798\n",
- "2016-11-04 18:33:32,870 Node[0] Epoch[15] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:33:32,871 Node[0] Epoch[15] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:33:32,872 Node[0] Epoch[15] Time cost=99.643\n",
- "2016-11-04 18:33:38,396 Node[0] Epoch[15] Validation-accuracy=nan\n",
- "2016-11-04 18:33:38,397 Node[0] Epoch[15] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:33:38,398 Node[0] Epoch[15] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:33:38,398 Node[0] Epoch[15] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:35:18,826 Node[0] Epoch[16] Train-accuracy=0.915234\n",
- "2016-11-04 18:35:18,827 Node[0] Epoch[16] Train-top_k_accuracy_5=0.997656\n",
- "2016-11-04 18:35:18,828 Node[0] Epoch[16] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:35:18,829 Node[0] Epoch[16] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:35:18,830 Node[0] Epoch[16] Time cost=100.431\n",
- "2016-11-04 18:35:24,759 Node[0] Epoch[16] Validation-accuracy=nan\n",
- "2016-11-04 18:35:24,759 Node[0] Epoch[16] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:35:24,760 Node[0] Epoch[16] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:35:24,761 Node[0] Epoch[16] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:37:04,619 Node[0] Epoch[17] Train-accuracy=0.918555\n",
- "2016-11-04 18:37:04,620 Node[0] Epoch[17] Train-top_k_accuracy_5=0.998437\n",
- "2016-11-04 18:37:04,621 Node[0] Epoch[17] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:37:04,621 Node[0] Epoch[17] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:37:04,622 Node[0] Epoch[17] Time cost=99.861\n",
- "2016-11-04 18:37:10,154 Node[0] Epoch[17] Validation-accuracy=nan\n",
- "2016-11-04 18:37:10,155 Node[0] Epoch[17] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:37:10,156 Node[0] Epoch[17] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:37:10,156 Node[0] Epoch[17] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:38:49,811 Node[0] Epoch[18] Train-accuracy=0.924479\n",
- "2016-11-04 18:38:49,812 Node[0] Epoch[18] Train-top_k_accuracy_5=0.998598\n",
- "2016-11-04 18:38:49,813 Node[0] Epoch[18] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:38:49,814 Node[0] Epoch[18] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:38:49,814 Node[0] Epoch[18] Time cost=99.658\n",
- "2016-11-04 18:38:55,339 Node[0] Epoch[18] Validation-accuracy=nan\n",
- "2016-11-04 18:38:55,340 Node[0] Epoch[18] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:38:55,340 Node[0] Epoch[18] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:38:55,341 Node[0] Epoch[18] Validation-top_k_accuracy_20=nan\n",
- "2016-11-04 18:40:35,220 Node[0] Epoch[19] Train-accuracy=0.927148\n",
- "2016-11-04 18:40:35,222 Node[0] Epoch[19] Train-top_k_accuracy_5=0.998828\n",
- "2016-11-04 18:40:35,222 Node[0] Epoch[19] Train-top_k_accuracy_10=1.000000\n",
- "2016-11-04 18:40:35,223 Node[0] Epoch[19] Train-top_k_accuracy_20=1.000000\n",
- "2016-11-04 18:40:35,224 Node[0] Epoch[19] Time cost=99.880\n",
- "2016-11-04 18:40:40,751 Node[0] Epoch[19] Validation-accuracy=nan\n",
- "2016-11-04 18:40:40,752 Node[0] Epoch[19] Validation-top_k_accuracy_5=nan\n",
- "2016-11-04 18:40:40,753 Node[0] Epoch[19] Validation-top_k_accuracy_10=nan\n",
- "2016-11-04 18:40:40,753 Node[0] Epoch[19] Validation-top_k_accuracy_20=nan\n"
- ]
- }
- ],
- "source": [
- "# Start training\n",
- "train_cifar10.do_train(args, \n",
- " callback_args=cb_args,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/example/module/train_cifar10.py b/example/module/train_cifar10.py
deleted file mode 100644
index a96e8d9284..0000000000
--- a/example/module/train_cifar10.py
+++ /dev/null
@@ -1,215 +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.
-
-"""Train CIFAR-10 classifier in MXNet.
-Demonstrates using the Module class.
-"""
-import logging
-import os, sys
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "image-classification")))
-
-import find_mxnet
-import mxnet as mx
-import argparse
-import train_model
-import importlib
-import platform
-
-
-def command_line_args(defaults=False):
- parser = argparse.ArgumentParser(description=__doc__,
- formatter_class=argparse.RawTextHelpFormatter)
- parser.add_argument('--network', type=str, default='inception-bn-28-small',
- help = 'which CNN style to use')
- my_dir = os.path.dirname(__file__)
- default_data_dir = os.path.abspath(os.path.join(my_dir, '..', 'image-classification', 'cifar10')) + '/'
- parser.add_argument('--data-dir', type=str, default=default_data_dir,
- help='the input data directory')
- parser.add_argument('--gpus', type=str,
- help='the gpus will be used, e.g "0,1,2,3"')
- parser.add_argument('--num-examples', type=int, default=60000,
- help='the number of training examples')
- parser.add_argument('--batch-size', type=int, default=128,
- help='the batch size')
- parser.add_argument('--lr', type=float, default=.05,
- help='the initial learning rate')
- parser.add_argument('--lr-factor', type=float, default=1,
- help='times the lr with a factor for every lr-factor-epoch epoch')
- parser.add_argument('--lr-factor-epoch', type=float, default=1,
- help='the number of epoch to factor the lr, could be .5')
- parser.add_argument('--model-prefix', type=str,
- help='the prefix of the model to load')
- parser.add_argument('--save-model-prefix', type=str,
- help='the prefix of the model to save')
- parser.add_argument('--num-epochs', type=int, default=20,
- help='the number of training epochs')
- parser.add_argument('--load-epoch', type=int,
- help="load the model on an epoch using the model-prefix")
- parser.add_argument('--kv-store', type=str, default='local',
- help='the kvstore type')
- if defaults:
- return parser.parse_args([])
- else:
- return parser.parse_args()
-
-
-# download data if necessary
-def _download(data_dir):
- if not os.path.isdir(data_dir):
- os.system("mkdir " + data_dir)
- cwd = os.path.abspath(os.getcwd())
- os.chdir(data_dir)
- if (not os.path.exists('train.rec')) or \
- (not os.path.exists('test.rec')) :
- import urllib, zipfile, glob
- dirname = os.getcwd()
- zippath = os.path.join(dirname, "cifar10.zip")
- urllib.urlretrieve("http://data.mxnet.io/mxnet/data/cifar10.zip", zippath)
- zf = zipfile.ZipFile(zippath, "r")
- zf.extractall()
- zf.close()
- os.remove(zippath)
- for f in glob.glob(os.path.join(dirname, "cifar", "*")):
- name = f.split(os.path.sep)[-1]
- os.rename(f, os.path.join(dirname, name))
- os.rmdir(os.path.join(dirname, "cifar"))
- os.chdir(cwd)
-
-# data
-def get_iterator(args, kv):
- data_shape = (3, 28, 28)
- data_dir = args.data_dir
- if os.name == "nt":
- data_dir = data_dir[:-1] + "\\"
- if '://' not in args.data_dir:
- _download(data_dir)
-
- train = mx.io.ImageRecordIter(
- path_imgrec = data_dir + "train.rec",
- mean_img = data_dir + "mean.bin",
- data_shape = data_shape,
- batch_size = args.batch_size,
- rand_crop = True,
- rand_mirror = True,
- num_parts = kv.num_workers,
- part_index = kv.rank)
-
- val = mx.io.ImageRecordIter(
- path_imgrec = data_dir + "test.rec",
- mean_img = data_dir + "mean.bin",
- rand_crop = False,
- rand_mirror = False,
- data_shape = data_shape,
- batch_size = args.batch_size,
- num_parts = kv.num_workers,
- part_index = kv.rank)
-
- return (train, val)
-
-
-def do_train(args, callback_args=None):
- # network
- net = importlib.import_module('symbol_' + args.network).get_symbol(10)
-
- my_dir = os.path.dirname(__file__)
- if args.model_prefix is not None:
- args.model_prefix = os.path.abspath(os.path.join(my_dir, args.model_prefix))
- if args.save_model_prefix is not None:
- args.save_model_prefix = os.path.abspath(os.path.join(my_dir, args.save_model_prefix))
-
-
- ################################################################################
- # train
- ################################################################################
-
- # kvstore
- kv = mx.kvstore.create(args.kv_store)
-
- # logging
- head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
- logging.basicConfig(level=logging.DEBUG, format=head)
- logging.info('start with arguments %s', args)
-
- logging.info('running on %s', platform.node())
-
- (train, val) = get_iterator(args, kv)
-
- if args.gpus is None or args.gpus == '':
- devs = mx.cpu()
- elif type(args.gpus) == str:
- devs = [mx.gpu(int(i)) for i in args.gpus.split(',')]
- else:
- devs = mx.gpu(int(args.gpus))
- logging.info('Starting with devices %s', devs)
-
- mod = mx.mod.Module(net, context=devs)
-
- # load model
- model_prefix = args.model_prefix
-
- if args.load_epoch is not None:
- assert model_prefix is not None
- logging.info('loading model from %s-%d...' % (model_prefix, args.load_epoch))
- sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, args.load_epoch)
- else:
- arg_params = None
- aux_params = None
-
- # save model
- save_model_prefix = args.save_model_prefix
- if save_model_prefix is None:
- save_model_prefix = model_prefix
- checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix)
-
- optim_args = {'learning_rate': args.lr, 'wd': 0.00001, 'momentum': 0.9}
- if 'lr_factor' in args and args.lr_factor < 1:
- optim_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
- step = max(int(epoch_size * args.lr_factor_epoch), 1),
- factor = args.lr_factor)
-
- if 'clip_gradient' in args and args.clip_gradient is not None:
- optim_args['clip_gradient'] = args.clip_gradient
-
- eval_metrics = ['accuracy']
- ## TopKAccuracy only allows top_k > 1
- for top_k in [5, 10, 20]:
- eval_metrics.append(mx.metric.create('top_k_accuracy', top_k = top_k))
-
- if args.load_epoch:
- begin_epoch = args.load_epoch+1
- else:
- begin_epoch = 0
-
- if not callback_args:
- callback_args = {
- 'batch_end_callback': mx.callback.Speedometer(args.batch_size, 50),
- 'epoch_end_callback': checkpoint,
- }
- else:
- pass
- #TODO: add checkpoint back in
-
- logging.info('start training for %d epochs...', args.num_epochs)
- mod.fit(train, eval_data=val, optimizer_params=optim_args,
- eval_metric=eval_metrics, num_epoch=args.num_epochs,
- arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch,
- **callback_args)
-
-if __name__ == "__main__":
- args = command_line_args()
- do_train(args)
-
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services