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
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+++ 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
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--- 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
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+++ 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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+
+ <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’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 >= 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">'#include
<std'</span>
+</pre></div>
+</div>
+</li>
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+
+ <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 ‘cifar-10-batches-py’
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 ‘vgg’ to
‘alexnet’ or ‘resnet’. 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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+
+ <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"><file></span></code>: the file of training list;</li>
+<li><code class="docutils literal"><span class="pre">-trainfolder</span> <span
class="pre"><folder></span></code>: the folder of training images;</li>
+<li><code class="docutils literal"><span class="pre">-testlist</span> <span
class="pre"><file></span></code>: the file of test list;</li>
+<li><code class="docutils literal"><span class="pre">-testfolder</span> <span
class="pre"><floder></span></code>: the folder of test images;</li>
+<li><code class="docutils literal"><span class="pre">-outdata</span> <span
class="pre"><folder></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"><int></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"><int></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"><float></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"><int></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"><int></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"><int></span></code>: number of training images;</li>
+<li><code class="docutils literal"><span class="pre">-ntest</span> <span
class="pre"><int></span></code>: number of test images;</li>
+<li><code class="docutils literal"><span class="pre">-data</span> <span
class="pre"><folder></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"><int></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"><int></span></code>: the number of threads to load data which
feed to the model.</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+</div>
+
+
+ </div>
+ </div>
+ <footer>
+
+ <div class="rst-footer-buttons" role="navigation" aria-label="footer
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+
+ <a href="../../../develop/schedule.html" class="btn btn-neutral
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+
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+
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+
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+ <p>
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+
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URL:
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--- incubator/singa/site/trunk/en/docs/examples/index.html (added)
+++ incubator/singa/site/trunk/en/docs/examples/index.html Tue Aug 16 07:32:01
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2"><a class="reference internal"
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+<li class="toctree-l2 current"><a class="current reference internal"
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+<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"
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+<li class="toctree-l3"><a class="reference internal"
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+</ul>
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+</li>
+</ul>
+<p class="caption"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal"
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+<li class="toctree-l1"><a class="reference internal"
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+<li class="toctree-l1"><a class="reference internal"
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+</ul>
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+<ul>
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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 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"
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+<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"
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+<li class="toctree-l3"><a class="reference internal"
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+</ul>
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+
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+
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+ <hr/>
+
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+ <p>
+ © Copyright 2016 The Apache Software Foundation. All rights
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+
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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’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>
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URL:
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+++ incubator/singa/site/trunk/en/py-modindex.html Tue Aug 16 07:32:01 2016
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+
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+ <a href="#cap-s"><strong>s</strong></a>
+ </div>
+
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+ <td>
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+ <tr class="cg-1">
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+ <td>
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+ <tr class="cg-1">
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+ <td>
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+ <a href="docs/utils.html#module-singa.utils"><code
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+
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reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa
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mentioned may be trademarks or registered trademarks of their respective
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