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 --><div class="section" id="train-alexnet-over-imagenet">
 <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">
 <h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="compile-singa">
 <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">
 <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">
 <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></span>    <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">
 <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></span>    <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>
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	<!--
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	or more contributor license agreements. See the NOTICE file
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	software distributed under the License is distributed on an
	"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	KIND, either express or implied. See the License for the
	specific language governing permissions and limitations
	under the License.
	--><div class="section" id="train-alexnet-over-imagenet">
	<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">
	<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
	<div class="section" id="compile-singa">
	<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">
	<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">
	<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></span> <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">
	<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></span> <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>
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