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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">&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-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">&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>


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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>
	</div>
	</div>
	</div>


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	<dd><a href="http://singa.apache.org/v0.3.0/">0.3</a></dd>
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