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From wang...@apache.org
Subject svn commit: r1782721 [16/24] - in /incubator/singa/site/trunk/v1.1.0: ./ _sources/ _sources/community/ _sources/develop/ _sources/docs/ _sources/docs/examples/ _sources/docs/examples/caffe/ _sources/docs/examples/char-rnn/ _sources/docs/examples/cifar1...
Date Mon, 13 Feb 2017 05:13:22 GMT
Added: incubator/singa/site/trunk/v1.1.0/docs/metric.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/metric.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/metric.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/metric.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,350 @@
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+          <li><a href="index.html">Documentation</a> &raquo;</li>
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+    <li>Metric</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
+        
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <div class="section" id="module-singa.metric">
+<span id="metric"></span><h1>Metric<a class="headerlink" href="#module-singa.metric" title="Permalink to this headline">¶</a></h1>
+<p>This module includes a set of metric classes for evaluating the model&#8217;s
+performance. The specific metric classes could be converted from C++
+implmentation or implemented directly using Python.</p>
+<p>Example usage:</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># randomly genearte the prediction activation</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>  <span class="c1"># normalize the prediction into probabilities</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span>  <span class="c1"># set the truth</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
+<span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># averaged accuracy over all 3 samples in x</span>
+</pre></div>
+</div>
+<dl class="class">
+<dt id="singa.metric.Metric">
+<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Metric</code><a class="headerlink" href="#singa.metric.Metric" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
+<p>Base metric class.</p>
+<p>Subclasses that wrap the C++ loss classes can use the inherited foward,
+and evaluate functions of this base class. Other subclasses need
+to override these functions. Users need to feed in the <strong>predictions</strong> and
+ground truth to get the metric values.</p>
+<dl class="method">
+<dt id="singa.metric.Metric.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.forward" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the metric for each sample.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; predictions, one row per sample</li>
+<li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; ground truth values, one row per sample</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a tensor of floats, one per sample</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.metric.Metric.evaluate">
+<code class="descname">evaluate</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.metric.Metric.evaluate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the averaged metric over all samples.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>x</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; predictions, one row per sample</li>
+<li><strong>y</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) &#8211; ground truth values, one row per sample</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a float value for the averaged metric</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.metric.Accuracy">
+<em class="property">class </em><code class="descclassname">singa.metric.</code><code class="descname">Accuracy</code><a class="headerlink" href="#singa.metric.Accuracy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#singa.metric.Metric" title="singa.metric.Metric"><code class="xref py py-class docutils literal"><span class="pre">singa.metric.Metric</span></code></a></p>
+<p>Compute the top one accuracy for singel label prediction tasks.</p>
+<p>It calls the C++ functions to do the calculation.</p>
+</dd></dl>
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Added: incubator/singa/site/trunk/v1.1.0/docs/model_zoo/caffe/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/model_zoo/caffe/README.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/model_zoo/caffe/README.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/model_zoo/caffe/README.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,276 @@
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+
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+    <li>Use parameters pre-trained from Caffe in SINGA</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
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+      </li>
+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
+<span id="use-parameters-pre-trained-from-caffe-in-singa"></span><h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
+<div class="section" id="run-this-example">
+<span id="run-this-example"></span><h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2>
+<p>You can run this example by simply executing <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
+The script does the following work.</p>
+<div class="section" id="obtain-the-caffe-model">
+<span id="obtain-the-caffe-model"></span><h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Download caffe model prototxt and parameter binary file.</li>
+<li>Currently we only support the latest caffe format, if your model is in
+previous version of caffe, please update it to current format.(This is
+supported by caffe)</li>
+<li>After updating, we can obtain two files, i.e., the prototxt and parameter
+binary file.</li>
+</ul>
+</div>
+<div class="section" id="prepare-test-images">
+<span id="prepare-test-images"></span><h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3>
+<p>A few sample images are downloaded into the <code class="docutils literal"><span class="pre">test</span></code> folder.</p>
+</div>
+<div class="section" id="predict">
+<span id="predict"></span><h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3>
+<p>The <code class="docutils literal"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
+<div class="highlight-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass
+</pre></div>
+</div>
+<p>where <code class="docutils literal"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:</p>
+<div class="highlight-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
+</pre></div>
+</div>
+<p>Then you type in the image path, and the program would output the top-5 labels.</p>
+<p>More Caffe models would be tested soon.</p>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+  
+
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+    <p>
+        &copy; Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
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+      </dl>
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Added: incubator/singa/site/trunk/v1.1.0/docs/model_zoo/char-rnn/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/model_zoo/char-rnn/README.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/model_zoo/char-rnn/README.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/model_zoo/char-rnn/README.html Mon Feb 13 05:13:19 2017
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+
+
+
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+  
+  <title>Train Char-RNN over plain text &mdash; incubator-singa 1.1.0 documentation</title>
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+        <link rel="next" title="Train AlexNet over ImageNet" href="../imagenet/alexnet/README.html"/>
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+<li class="toctree-l2"><a class="reference internal" href="../../loss.html">Loss</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../optimizer.html">Optimizer</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../image_tool.html">Image Tool</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../snapshot.html">Snapshot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../converter.html">Caffe Converter</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../utils.html">Utils</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Model Zoo</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../cifar10/README.html">Train CNN over Cifar-10</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="">Train Char-RNN over plain text</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">Image Classification using GoogleNet</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p class="caption"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/schedule.html">Development Schedule</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/how-contribute.html">How to Contribute to SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/contribute-code.html">How to Contribute Code</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/contribute-docs.html">How to Contribute to Documentation</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Community</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../community/source-repository.html">Source Repository</a></li>
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+      
+    <li>Train Char-RNN over plain text</li>
+      <li class="wy-breadcrumbs-aside">
+        
+          
+        
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <div class="section" id="train-char-rnn-over-plain-text">
+<span id="train-char-rnn-over-plain-text"></span><h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
+<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGA&#8217;s RNN layers.
+We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.</p>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version &gt;= 5.05.</p>
+</li>
+<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
+Other plain text files can also be used.</p>
+</li>
+<li><p class="first">Start the training,</p>
+<div class="highlight-python"><div class="highlight"><pre>  python train.py linux_input.txt
+</pre></div>
+</div>
+<p>Some hyper-parameters could be set through command line,</p>
+<div class="highlight-python"><div class="highlight"><pre>  python train.py -h
+</pre></div>
+</div>
+</li>
+<li><p class="first">Sample characters from the model by providing the number of characters to sample and the seed string.</p>
+<div class="highlight-python"><div class="highlight"><pre>  python sample.py &#39;model.bin&#39; 100 --seed &#39;#include &lt;std&#39;
+</pre></div>
+</div>
+<p>Please replace &#8216;model.bin&#8217; with the path to one of the checkpoint paths.</p>
+</li>
+</ul>
+</div>
+</div>
+
+
+           </div>
+          </div>
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+      
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+        &copy; Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
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+      </dl>
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Added: incubator/singa/site/trunk/v1.1.0/docs/model_zoo/cifar10/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/model_zoo/cifar10/README.html?rev=1782721&view=auto
==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/model_zoo/cifar10/README.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/model_zoo/cifar10/README.html Mon Feb 13 05:13:19 2017
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+
+
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+<li class="toctree-l2"><a class="reference internal" href="../../net.html">FeedForward Net</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../loss.html">Loss</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../optimizer.html">Optimizer</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../image_tool.html">Image Tool</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../snapshot.html">Snapshot</a></li>
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+<li class="toctree-l3 current"><a class="current reference internal" href="">Train CNN over Cifar-10</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../char-rnn/README.html">Train Char-RNN over plain text</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">Image Classification using GoogleNet</a></li>
+</ul>
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+           <div itemprop="articleBody">
+            
+  <div class="section" id="train-cnn-over-cifar-10">
+<span id="train-cnn-over-cifar-10"></span><h1>Train CNN over Cifar-10<a class="headerlink" href="#train-cnn-over-cifar-10" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward artificial neural
+network widely used for image and video classification. In this example, we
+will train three deep CNN models to do image classification for the CIFAR-10 dataset,</p>
+<ol class="simple">
+<li><a class="reference external" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg">AlexNet</a>
+the best validation accuracy (without data augmentation) we achieved was about 82%.</li>
+<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>, the best validation accuracy (without data augmentation) we achieved was about 89%.</li>
+<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch">ResNet</a>, the best validation accuracy (without data augmentation) we achieved was about 83%.</li>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li>
+</ol>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa-installation">
+<span id="singa-installation"></span><h3>SINGA installation<a class="headerlink" href="#singa-installation" title="Permalink to this headline">¶</a></h3>
+<p>Users can compile and install SINGA from source or install the Python version.
+The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5)
+are required. Please refer to the installation page for detailed instructions.</p>
+</div>
+<div class="section" id="data-preparation">
+<span id="data-preparation"></span><h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3>
+<p>The binary Cifar-10 dataset could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py bin
+</pre></div>
+</div>
+<p>The Python version could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py py
+</pre></div>
+</div>
+</div>
+<div class="section" id="training">
+<span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<p>There are four training programs</p>
+<ol>
+<li><p class="first">train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in &#8216;cifar-10-batches-py&#8217; folder.</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py
+</pre></div>
+</div>
+<p>To train other models, please replace &#8216;vgg&#8217; to &#8216;alexnet&#8217;, &#8216;resnet&#8217; or &#8216;caffe&#8217;,
+where &#8216;caffe&#8217; refers to the alexnet model converted from Caffe. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py  --use_cpu
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices.
+The two devices run synchronously to compute the gradients of the mode parameters, which are
+averaged on the host CPU device and then be applied to update the parameters.</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run-parallel.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.</p>
+</li>
+</ol>
+</div>
+<div class="section" id="prediction">
+<span id="prediction"></span><h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3>
+<p>predict.py includes the prediction function</p>
+<div class="highlight-python"><div class="highlight"><pre>    def predict(net, images, dev, topk=5)
+</pre></div>
+</div>
+<p>The net is created by loading the previously trained model; Images consist of
+a numpy array of images (one row per image); dev is the training device, e.g.,
+a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p>
+<p>The predict.py file&#8217;s main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The &#8216;model.bin&#8217; file generated by the training program should be placed at the cifar10 folder to run</p>
+<div class="highlight-python"><div class="highlight"><pre>    python predict.py
+</pre></div>
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+            
+  <div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
+<span id="use-parameters-pre-trained-from-caffe-in-singa"></span><h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
+<div class="section" id="run-this-example">
+<span id="run-this-example"></span><h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2>
+<p>You can run this example by simply executing <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
+The script does the following work.</p>
+<div class="section" id="obtain-the-caffe-model">
+<span id="obtain-the-caffe-model"></span><h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Download caffe model prototxt and parameter binary file.</li>
+<li>Currently we only support the latest caffe format, if your model is in
+previous version of caffe, please update it to current format.(This is
+supported by caffe)</li>
+<li>After updating, we can obtain two files, i.e., the prototxt and parameter
+binary file.</li>
+</ul>
+</div>
+<div class="section" id="prepare-test-images">
+<span id="prepare-test-images"></span><h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3>
+<p>A few sample images are downloaded into the <code class="docutils literal"><span class="pre">test</span></code> folder.</p>
+</div>
+<div class="section" id="predict">
+<span id="predict"></span><h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3>
+<p>The <code class="docutils literal"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
+<div class="highlight-python"><div class="highlight"><pre>usage: predict.py [-h] model_txt model_bin imgclass
+</pre></div>
+</div>
+<p>where <code class="docutils literal"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:</p>
+<div class="highlight-python"><div class="highlight"><pre>python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt
+</pre></div>
+</div>
+<p>Then you type in the image path, and the program would output the top-5 labels.</p>
+<p>More Caffe models would be tested soon.</p>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+  
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+        &copy; Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
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+  <div class="section" id="train-char-rnn-over-plain-text">
+<span id="train-char-rnn-over-plain-text"></span><h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
+<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGA&#8217;s RNN layers.
+We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.</p>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version &gt;= 5.05.</p>
+</li>
+<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
+Other plain text files can also be used.</p>
+</li>
+<li><p class="first">Start the training,</p>
+<div class="highlight-python"><div class="highlight"><pre>  python train.py linux_input.txt
+</pre></div>
+</div>
+<p>Some hyper-parameters could be set through command line,</p>
+<div class="highlight-python"><div class="highlight"><pre>  python train.py -h
+</pre></div>
+</div>
+</li>
+<li><p class="first">Sample characters from the model by providing the number of characters to sample and the seed string.</p>
+<div class="highlight-python"><div class="highlight"><pre>  python sample.py &#39;model.bin&#39; 100 --seed &#39;#include &lt;std&#39;
+</pre></div>
+</div>
+<p>Please replace &#8216;model.bin&#8217; with the path to one of the checkpoint paths.</p>
+</li>
+</ul>
+</div>
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+        &copy; Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
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Added: incubator/singa/site/trunk/v1.1.0/docs/model_zoo/examples/cifar10/README.html
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==============================================================================
--- incubator/singa/site/trunk/v1.1.0/docs/model_zoo/examples/cifar10/README.html (added)
+++ incubator/singa/site/trunk/v1.1.0/docs/model_zoo/examples/cifar10/README.html Mon Feb 13 05:13:19 2017
@@ -0,0 +1,321 @@
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+        
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+           <div itemprop="articleBody">
+            
+  <div class="section" id="train-cnn-over-cifar-10">
+<span id="train-cnn-over-cifar-10"></span><h1>Train CNN over Cifar-10<a class="headerlink" href="#train-cnn-over-cifar-10" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward artificial neural
+network widely used for image and video classification. In this example, we
+will train three deep CNN models to do image classification for the CIFAR-10 dataset,</p>
+<ol class="simple">
+<li><a class="reference external" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg">AlexNet</a>
+the best validation accuracy (without data augmentation) we achieved was about 82%.</li>
+<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>, the best validation accuracy (without data augmentation) we achieved was about 89%.</li>
+<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch">ResNet</a>, the best validation accuracy (without data augmentation) we achieved was about 83%.</li>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li>
+</ol>
+<div class="section" id="instructions">
+<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa-installation">
+<span id="singa-installation"></span><h3>SINGA installation<a class="headerlink" href="#singa-installation" title="Permalink to this headline">¶</a></h3>
+<p>Users can compile and install SINGA from source or install the Python version.
+The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5)
+are required. Please refer to the installation page for detailed instructions.</p>
+</div>
+<div class="section" id="data-preparation">
+<span id="data-preparation"></span><h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3>
+<p>The binary Cifar-10 dataset could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py bin
+</pre></div>
+</div>
+<p>The Python version could be downloaded by</p>
+<div class="highlight-python"><div class="highlight"><pre>python download_data.py py
+</pre></div>
+</div>
+</div>
+<div class="section" id="training">
+<span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<p>There are four training programs</p>
+<ol>
+<li><p class="first">train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in &#8216;cifar-10-batches-py&#8217; folder.</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py
+</pre></div>
+</div>
+<p>To train other models, please replace &#8216;vgg&#8217; to &#8216;alexnet&#8217;, &#8216;resnet&#8217; or &#8216;caffe&#8217;,
+where &#8216;caffe&#8217; refers to the alexnet model converted from Caffe. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</p>
+<div class="highlight-python"><div class="highlight"><pre> python train.py vgg cifar-10-batches-py  --use_cpu
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices.
+The two devices run synchronously to compute the gradients of the mode parameters, which are
+averaged on the host CPU device and then be applied to update the parameters.</p>
+<div class="highlight-python"><div class="highlight"><pre> ./run-parallel.sh
+</pre></div>
+</div>
+</li>
+<li><p class="first">vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.</p>
+</li>
+</ol>
+</div>
+<div class="section" id="prediction">
+<span id="prediction"></span><h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3>
+<p>predict.py includes the prediction function</p>
+<div class="highlight-python"><div class="highlight"><pre>    def predict(net, images, dev, topk=5)
+</pre></div>
+</div>
+<p>The net is created by loading the previously trained model; Images consist of
+a numpy array of images (one row per image); dev is the training device, e.g.,
+a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p>
+<p>The predict.py file&#8217;s main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The &#8216;model.bin&#8217; file generated by the training program should be placed at the cifar10 folder to run</p>
+<div class="highlight-python"><div class="highlight"><pre>    python predict.py
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+  
+
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+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2017 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners..
+
+    </p>
+  </div>
+  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 
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