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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-python"><div class="highlight"><pre> sh create_data.sh
+</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-python"><div class="highlight"><pre> sh run.sh
+</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>
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+<p>name: GoogleNet on ImageNet
+SINGA version: 1.0.1
+SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
+license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</p>
+<hr class="docutils" />
+<div class="section" id="image-classification-using-googlenet">
+<span id="image-classification-using-googlenet"></span><h1>Image Classification using GoogleNet<a class="headerlink" href="#image-classification-using-googlenet" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.</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">Download the parameter checkpoint file into this folder</p>
+<div class="highlight-python"><div class="highlight"><pre> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+ $ tar xvf bvlc_googlenet.tar.gz
+</pre></div>
+</div>
+</li>
+<li><p class="first">Run the program</p>
+<div class="highlight-python"><div class="highlight"><pre> # use cpu
+ $ python serve.py -C &
+ # use gpu
+ $ python serve.py &
+</pre></div>
+</div>
+</li>
+<li><p class="first">Submit images for classification</p>
+<div class="highlight-python"><div class="highlight"><pre> $ curl -i -F image=@image1.jpg http://localhost:9999/api
+ $ curl -i -F image=@image2.jpg http://localhost:9999/api
+ $ curl -i -F image=@image3.jpg http://localhost:9999/api
+</pre></div>
+</div>
+</li>
+</ul>
+<p>image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.</p>
+</div>
+<div class="section" id="details">
+<span id="details"></span><h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
+<p>We first extract the parameter values from <a class="reference external" href="http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel">Caffe’s checkpoint file</a> into a pickle version
+After downloading the checkpoint file into <code class="docutils literal"><span class="pre">caffe_root/python</span></code> folder, run the following script</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="c1"># to be executed within caffe_root/python folder</span>
+<span class="kn">import</span> <span class="nn">caffe</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">cPickle</span> <span class="kn">as</span> <span class="nn">pickle</span>
+
+<span class="n">model_def</span> <span class="o">=</span> <span class="s1">'../models/bvlc_googlenet/deploy.prototxt'</span>
+<span class="n">weight</span> <span class="o">=</span> <span class="s1">'bvlc_googlenet.caffemodel'</span> <span class="c1"># must be downloaded at first</span>
+<span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="n">model_def</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+ <span class="n">weights</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+ <span class="n">bias</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+ <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_weight'</span><span class="p">]</span><span class="o">=</span><span class="n">weights</span>
+ <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_bias'</span><span class="p">]</span><span class="o">=</span><span class="n">bias</span>
+ <span class="k">print</span> <span class="n">layer_name</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span>
+
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'bvlc_googlenet.pickle'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
+ <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">fd</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Then we construct the GoogleNet using SINGA’s FeedForwardNet structure.
+Note that we added a EndPadding layer to resolve the issue from discrepancy
+of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
+Only the MaxPooling layers outside inception blocks have this problem.
+Refer to <a class="reference external" href="http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html">this</a> for more detials.</p>
+</div>
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+ <div class="section" id="model-zoo">
+<h1>Model Zoo<a class="headerlink" href="#model-zoo" 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#data-preparation">Data preparation</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/alexnet/README.html">Train AlexNet over ImageNet</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="imagenet/alexnet/README.html#instructions">Instructions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html#compile-singa">Compile SINGA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html#data-download">Data download</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html#data-preprocessing">Data preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html#training">Training</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/googlenet/README.html">Image Classification using GoogleNet</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="imagenet/googlenet/README.html#instructions">Instructions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="imagenet/googlenet/README.html#details">Details</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
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+ © 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>
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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-python"><div class="highlight"><pre> python train.py mnist.pkl.gz
+</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-python"><div class="highlight"><pre> python train.py mnist.pkl.gz --use_gpu
+</pre></div>
+</div>
+</div>
+</div>
+
+
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+ </div>
+ <footer>
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+ © 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-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-python"><div class="highlight"><pre> sh create_data.sh
+</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-python"><div class="highlight"><pre> sh run.sh
+</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>
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+<li class="toctree-l3 current"><a class="current reference internal" href="">Train AlexNet over ImageNet</a><ul>
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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-python"><div class="highlight"><pre> sh create_data.sh
+</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-python"><div class="highlight"><pre> sh run.sh
+</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>
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