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From wang...@apache.org
Subject svn commit: r1756486 - in /incubator/singa/site/trunk/en: ./ _sources/docs/examples/ _sources/docs/examples/char-rnn/ _sources/docs/examples/cifar10/ _sources/docs/examples/imagenet/ _sources/docs/examples/mnist/ docs/examples/ docs/examples/char-rnn/ ...
Date Tue, 16 Aug 2016 07:32:01 GMT
Author: wangwei
Date: Tue Aug 16 07:32:01 2016
New Revision: 1756486

URL: http://svn.apache.org/viewvc?rev=1756486&view=rev
Log:
add docs for examples.

Added:
    incubator/singa/site/trunk/en/_sources/docs/examples/
    incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/
    incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/README.txt
    incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/
    incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/README.txt
    incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/
    incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/README.txt
    incubator/singa/site/trunk/en/_sources/docs/examples/index.txt
    incubator/singa/site/trunk/en/_sources/docs/examples/mnist/
    incubator/singa/site/trunk/en/_sources/docs/examples/mnist/README.txt
    incubator/singa/site/trunk/en/docs/examples/
    incubator/singa/site/trunk/en/docs/examples/char-rnn/
    incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html
    incubator/singa/site/trunk/en/docs/examples/cifar10/
    incubator/singa/site/trunk/en/docs/examples/cifar10/README.html
    incubator/singa/site/trunk/en/docs/examples/imagenet/
    incubator/singa/site/trunk/en/docs/examples/imagenet/README.html
    incubator/singa/site/trunk/en/docs/examples/index.html
    incubator/singa/site/trunk/en/docs/examples/mnist/
    incubator/singa/site/trunk/en/docs/examples/mnist/README.html
    incubator/singa/site/trunk/en/py-modindex.html

Added: incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/README.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/README.txt?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/README.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/examples/char-rnn/README.txt Tue Aug 16 07:32:01 2016
@@ -0,0 +1,30 @@
+# Train Char-RNN over plain text
+
+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's RNN layers.
+We will use the [char-rnn](https://github.com/karpathy/char-rnn) 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.
+
+
+## Instructions
+
+* Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.
+
+* Prepare the dataset. Download the [kernel source code](http://cs.stanford.edu/people/karpathy/char-rnn/).
+Other plain text files can also be used.
+
+* Start the training,
+
+        python train.py input_linux.txt
+
+  Some hyper-parameters could be set through command line,
+
+        python train.py -h
+
+* Sample characters from the model by providing the number of characters to sample and the seed string.
+
+        python sample.py 100 --seed '#include <std'

Added: incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/README.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/README.txt?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/README.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/examples/cifar10/README.txt Tue Aug 16 07:32:01 2016
@@ -0,0 +1,69 @@
+# Train CNN over Cifar-10
+
+
+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,
+
+1. [AlexNet](https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg)
+the best validation accuracy (without data augmentation) we achieved was about 82%.
+
+2. [VGGNet](http://torch.ch/blog/2015/07/30/cifar.html), the best validation accuracy (without data augmentation) we achieved was about 89%.
+3. [ResNet](https://github.com/facebook/fb.resnet.torch), the best validation accuracy (without data augmentation) we achieved was about 83%.
+
+
+## Instructions
+
+
+### SINGA installation
+
+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.
+
+
+
+### Training
+
+There are four training programs
+
+1. train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in 'cifar-10-batches-py' folder.
+
+        python train.py vgg cifar-10-batches-py
+
+    To train other models, please replace 'vgg' to 'alexnet' or 'resnet'. By default
+    the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+    argument
+
+        python train.py vgg cifar-10-batches-py  --use_cpu
+
+2. alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,
+
+        run.sh
+
+3. 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.
+
+        run-parallel.sh
+
+4. vgg-parallel.cc. It train the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.
+
+### Prediction
+
+predict.py includes the prediction function
+
+        def predict(net, images, dev, topk=5)
+
+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; topk labels of each image would be
+returned.
+
+
+
+
+
+
+

Added: incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/README.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/README.txt?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/README.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/examples/imagenet/README.txt Tue Aug 16 07:32:01 2016
@@ -0,0 +1,58 @@
+# Train AlexNet over ImageNet
+
+Convolution neural network (CNN) is a type of feed-forward neural
+network widely used for image and video classification. In this example, we will
+use a [deep CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
+to do image classification against the ImageNet dataset.
+
+## Instructions
+
+### Compile SINGA
+
+Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
+options in CMakeLists.txt or run `ccmake ..` in build/ folder.
+
+We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)
+
+### Data download
+* Please refer to step1-3 on [Instructions to create ImageNet 2012 data](https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data)
+  to download and decompress the data.
+* You can download the training and validation list by
+  [get_ilsvrc_aux.sh](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
+  or from [Imagenet](http://www.image-net.org/download-images).
+
+### Data preprocessing
+* Assuming you have downloaded the data and the list.
+  Now we should transform the data into binary files. You can run:
+
+          sh create_data.sh
+
+  The script will generate a test file(`test.bin`), a mean file(`mean.bin`) and
+  several training files(`trainX.bin`) in the specified output folder.
+* You can also change the parameters in `create_data.sh`.
+  + `-trainlist <file>`: the file of training list;
+  + `-trainfolder <folder>`: the folder of training images;
+  + `-testlist <file>`: the file of test list;
+  + `-testfolder <floder>`: the folder of test images;
+  + `-outdata <folder>`: the folder to save output files, including mean, training and test files.
+    The script will generate these files in the specified folder;
+  + `-filesize <int>`: number of training images that stores in each binary file.
+
+### Training
+* After preparing data, you can run the following command to train the Alexnet model.
+
+          sh run.sh
+
+* You may change the parameters in `run.sh`.
+  + `-epoch <int>`: number of epoch to be trained, default is 90;
+  + `-lr <float>`: base learning rate, the learning rate will decrease each 20 epochs,
+    more specifically, `lr = lr * exp(0.1 * (epoch / 20))`;
+  + `-batchsize <int>`: batchsize, it should be changed regarding to your memory;
+  + `-filesize <int>`: number of training images that stores in each binary file, it is the
+    same as the `filesize` in data preprocessing;
+  + `-ntrain <int>`: number of training images;
+  + `-ntest <int>`: number of test images;
+  + `-data <folder>`: the folder which stores the binary files, it is exactly the output
+    folder in data preprocessing step;
+  + `-pfreq <int>`: the frequency(in batch) of printing current model status(loss and accuracy);
+  + `-nthreads <int>`: the number of threads to load data which feed to the model.

Added: incubator/singa/site/trunk/en/_sources/docs/examples/index.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/docs/examples/index.txt?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/docs/examples/index.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/examples/index.txt Tue Aug 16 07:32:01 2016
@@ -0,0 +1,10 @@
+Examples
+========
+
+.. toctree::
+
+   cifar10/README
+   char-rnn/README
+   imagenet/README
+
+

Added: incubator/singa/site/trunk/en/_sources/docs/examples/mnist/README.txt
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/_sources/docs/examples/mnist/README.txt?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/_sources/docs/examples/mnist/README.txt (added)
+++ incubator/singa/site/trunk/en/_sources/docs/examples/mnist/README.txt Tue Aug 16 07:32:01 2016
@@ -0,0 +1,18 @@
+# Train a RBM model against MNIST dataset
+
+This example is to train an RBM model using the
+MNIST dataset. The RBM model and its hyper-parameters are set following
+[Hinton's paper](http://www.cs.toronto.edu/~hinton/science.pdf)
+
+## Running instructions
+
+1. Download the pre-processed [MNIST dataset](https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz)
+
+2. Start the training
+
+        python train.py
+
+By default the training code would run on CPU. To run it on a GPU card, please start
+the program with an additional argument
+
+        python train.py --use_gpu

Added: incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html?rev=1756486&view=auto
==============================================================================
--- incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html (added)
+++ incubator/singa/site/trunk/en/docs/examples/char-rnn/README.html Tue Aug 16 07:32:01 2016
@@ -0,0 +1,306 @@
+
+
+
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+<head>
+  <meta charset="utf-8">
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+  
+  <title>Train Char-RNN over plain text &mdash; incubator-singa 1.0.0 documentation</title>
+  
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+        <link rel="up" title="Examples" href="../index.html"/>
+        <link rel="next" title="Train AlexNet over ImageNet" href="../imagenet/README.html"/>
+        <link rel="prev" title="Train CNN over Cifar-10" href="../cifar10/README.html"/>
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+<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>
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+<li class="toctree-l3"><a class="reference internal" href="../imagenet/README.html">Train AlexNet over ImageNet</a></li>
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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-default"><div class="highlight"><pre>  <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">input_linux</span><span class="o">.</span><span class="n">txt</span>
+</pre></div>
+</div>
+<p>Some hyper-parameters could be set through command line,</p>
+<div class="highlight-default"><div class="highlight"><pre>  <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">h</span>
+</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-default"><div class="highlight"><pre>  <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s">&#39;#include &lt;std&#39;</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</div>
+</div>
+
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--- incubator/singa/site/trunk/en/docs/examples/cifar10/README.html (added)
+++ incubator/singa/site/trunk/en/docs/examples/cifar10/README.html Tue Aug 16 07:32:01 2016
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+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Examples</a><ul class="current">
+<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>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../char-rnn/README.html">Train Char-RNN over plain text</a></li>
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+</ul>
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+      <li class="wy-breadcrumbs-aside">
+        
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+  </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>
+</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="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-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>To train other models, please replace &#8216;vgg&#8217; to &#8216;alexnet&#8217; or &#8216;resnet&#8217;. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</p>
+<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span>  <span class="o">--</span><span class="n">use_cpu</span>
+</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-default"><div class="highlight"><pre> <span class="n">run</span><span class="o">.</span><span class="n">sh</span>
+</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-default"><div class="highlight"><pre> <span class="n">run</span><span class="o">-</span><span class="n">parallel</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+</li>
+<li><p class="first">vgg-parallel.cc. It train 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-default"><div class="highlight"><pre>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">,</span> <span class="n">dev</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+</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; topk labels of each image would be
+returned.</p>
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+        &copy; Copyright 2016 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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--- incubator/singa/site/trunk/en/docs/examples/imagenet/README.html (added)
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+            
+  <div class="section" id="train-alexnet-over-imagenet">
+<span id="train-alexnet-over-imagenet"></span><h1>Train AlexNet over ImageNet<a class="headerlink" href="#train-alexnet-over-imagenet" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward neural
+network widely used for image and video classification. In this example, we will
+use a <a class="reference external" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">deep CNN model</a>
+to do image classification against the ImageNet dataset.</p>
+<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="compile-singa">
+<span id="compile-singa"></span><h3>Compile SINGA<a class="headerlink" href="#compile-singa" title="Permalink to this headline">¶</a></h3>
+<p>Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
+options in CMakeLists.txt or run <code class="docutils literal"><span class="pre">ccmake</span> <span class="pre">..</span></code> in build/ folder.</p>
+<p>We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)</p>
+</div>
+<div class="section" id="data-download">
+<span id="data-download"></span><h3>Data download<a class="headerlink" href="#data-download" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Please refer to step1-3 on <a class="reference external" href="https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data">Instructions to create ImageNet 2012 data</a>
+to download and decompress the data.</li>
+<li>You can download the training and validation list by
+<a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh">get_ilsvrc_aux.sh</a>
+or from <a class="reference external" href="http://www.image-net.org/download-images">Imagenet</a>.</li>
+</ul>
+</div>
+<div class="section" id="data-preprocessing">
+<span id="data-preprocessing"></span><h3>Data preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">Assuming you have downloaded the data and the list.
+Now we should transform the data into binary files. You can run:</p>
+<div class="highlight-default"><div class="highlight"><pre>    <span class="n">sh</span> <span class="n">create_data</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>The script will generate a test file(<code class="docutils literal"><span class="pre">test.bin</span></code>), a mean file(<code class="docutils literal"><span class="pre">mean.bin</span></code>) and
+several training files(<code class="docutils literal"><span class="pre">trainX.bin</span></code>) in the specified output folder.</p>
+</li>
+<li><p class="first">You can also change the parameters in <code class="docutils literal"><span class="pre">create_data.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">-trainlist</span> <span class="pre">&lt;file&gt;</span></code>: the file of training list;</li>
+<li><code class="docutils literal"><span class="pre">-trainfolder</span> <span class="pre">&lt;folder&gt;</span></code>: the folder of training images;</li>
+<li><code class="docutils literal"><span class="pre">-testlist</span> <span class="pre">&lt;file&gt;</span></code>: the file of test list;</li>
+<li><code class="docutils literal"><span class="pre">-testfolder</span> <span class="pre">&lt;floder&gt;</span></code>: the folder of test images;</li>
+<li><code class="docutils literal"><span class="pre">-outdata</span> <span class="pre">&lt;folder&gt;</span></code>: the folder to save output files, including mean, training and test files.
+The script will generate these files in the specified folder;</li>
+<li><code class="docutils literal"><span class="pre">-filesize</span> <span class="pre">&lt;int&gt;</span></code>: number of training images that stores in each binary file.</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="section" id="training">
+<span id="training"></span><h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">After preparing data, you can run the following command to train the Alexnet model.</p>
+<div class="highlight-default"><div class="highlight"><pre>    <span class="n">sh</span> <span class="n">run</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+</li>
+<li><p class="first">You may change the parameters in <code class="docutils literal"><span class="pre">run.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal"><span class="pre">-epoch</span> <span class="pre">&lt;int&gt;</span></code>: number of epoch to be trained, default is 90;</li>
+<li><code class="docutils literal"><span class="pre">-lr</span> <span class="pre">&lt;float&gt;</span></code>: base learning rate, the learning rate will decrease each 20 epochs,
+more specifically, <code class="docutils literal"><span class="pre">lr</span> <span class="pre">=</span> <span class="pre">lr</span> <span class="pre">*</span> <span class="pre">exp(0.1</span> <span class="pre">*</span> <span class="pre">(epoch</span> <span class="pre">/</span> <span class="pre">20))</span></code>;</li>
+<li><code class="docutils literal"><span class="pre">-batchsize</span> <span class="pre">&lt;int&gt;</span></code>: batchsize, it should be changed regarding to your memory;</li>
+<li><code class="docutils literal"><span class="pre">-filesize</span> <span class="pre">&lt;int&gt;</span></code>: number of training images that stores in each binary file, it is the
+same as the <code class="docutils literal"><span class="pre">filesize</span></code> in data preprocessing;</li>
+<li><code class="docutils literal"><span class="pre">-ntrain</span> <span class="pre">&lt;int&gt;</span></code>: number of training images;</li>
+<li><code class="docutils literal"><span class="pre">-ntest</span> <span class="pre">&lt;int&gt;</span></code>: number of test images;</li>
+<li><code class="docutils literal"><span class="pre">-data</span> <span class="pre">&lt;folder&gt;</span></code>: the folder which stores the binary files, it is exactly the output
+folder in data preprocessing step;</li>
+<li><code class="docutils literal"><span class="pre">-pfreq</span> <span class="pre">&lt;int&gt;</span></code>: the frequency(in batch) of printing current model status(loss and accuracy);</li>
+<li><code class="docutils literal"><span class="pre">-nthreads</span> <span class="pre">&lt;int&gt;</span></code>: the number of threads to load data which feed to the model.</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
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+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="../../../develop/schedule.html" class="btn btn-neutral float-right" title="Development Schedule" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
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+        &copy; Copyright 2016 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/en/docs/examples/index.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/examples/index.html?rev=1756486&view=auto
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+++ incubator/singa/site/trunk/en/docs/examples/index.html Tue Aug 16 07:32:01 2016
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../installation.html">Installation</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../device.html">Device</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tensor.html">Tensor</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../optimizer.html">Optimizer</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Examples</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html">Train CNN over Cifar-10</a></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/README.html">Train AlexNet over ImageNet</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 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>
+<li class="toctree-l1"><a class="reference internal" href="../../community/mail-lists.html">Project Mailing Lists</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../community/team-list.html">The SINGA Team</a></li>
+</ul>
+
+            
+          
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+           <div itemprop="articleBody">
+            
+  <div class="section" id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="cifar10/README.html">Train CNN over Cifar-10</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="cifar10/README.html#instructions">Instructions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#singa-installation">SINGA installation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#training">Training</a></li>
+<li class="toctree-l3"><a class="reference internal" href="cifar10/README.html#prediction">Prediction</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="char-rnn/README.html">Train Char-RNN over plain text</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="char-rnn/README.html#instructions">Instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/README.html">Train AlexNet over ImageNet</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="imagenet/README.html#instructions">Instructions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#compile-singa">Compile SINGA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#data-download">Data download</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#data-preprocessing">Data preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/README.html#training">Training</a></li>
+</ul>
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+      
+    </div>
+  
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+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2016 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>
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Added: incubator/singa/site/trunk/en/docs/examples/mnist/README.html
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+  <ul class="wy-breadcrumbs">
+    <li><a href="../../../index.html">Docs</a> &raquo;</li>
+      
+    <li>Train a RBM model against MNIST dataset</li>
+      <li class="wy-breadcrumbs-aside">
+        
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+            
+  <div class="section" id="train-a-rbm-model-against-mnist-dataset">
+<span id="train-a-rbm-model-against-mnist-dataset"></span><h1>Train a RBM model against MNIST dataset<a class="headerlink" href="#train-a-rbm-model-against-mnist-dataset" title="Permalink to this headline">¶</a></h1>
+<p>This example is to train an RBM model using the
+MNIST dataset. The RBM model and its hyper-parameters are set following
+<a class="reference external" href="http://www.cs.toronto.edu/~hinton/science.pdf">Hinton&#8217;s paper</a></p>
+<div class="section" id="running-instructions">
+<span id="running-instructions"></span><h2>Running instructions<a class="headerlink" href="#running-instructions" title="Permalink to this headline">¶</a></h2>
+<ol>
+<li><p class="first">Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz">MNIST dataset</a></p>
+</li>
+<li><p class="first">Start the training</p>
+<div class="highlight-default"><div class="highlight"><pre> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+</li>
+</ol>
+<p>By default the training code would run on CPU. To run it on a GPU card, please start
+the program with an additional argument</p>
+<div class="highlight-default"><div class="highlight"><pre>    <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">--</span><span class="n">use_gpu</span>
+</pre></div>
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+++ incubator/singa/site/trunk/en/py-modindex.html Tue Aug 16 07:32:01 2016
@@ -0,0 +1,296 @@
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+   <h1>Python Module Index</h1>
+
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+   <a href="#cap-s"><strong>s</strong></a>
+   </div>
+
+   <table class="indextable modindextable" cellspacing="0" cellpadding="2">
+     <tr class="pcap"><td></td><td>&nbsp;</td><td></td></tr>
+     <tr class="cap" id="cap-s"><td></td><td>
+       <strong>s</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">singa</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/device.html#module-singa.device"><code class="xref">singa.device</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/initializer.html#module-singa.initializer"><code class="xref">singa.initializer</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/layer.html#module-singa.layer"><code class="xref">singa.layer</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/loss.html#module-singa.loss"><code class="xref">singa.loss</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/metric.html#module-singa.metric"><code class="xref">singa.metric</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/optimizer.html#module-singa.optimizer"><code class="xref">singa.optimizer</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/tensor.html#module-singa.tensor"><code class="xref">singa.tensor</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&nbsp;&nbsp;&nbsp;
+       <a href="docs/utils.html#module-singa.utils"><code class="xref">singa.utils</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
+           </div>
+          </div>
+          <footer>
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+
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+        &copy; Copyright 2016 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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