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+ <!--
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
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+ "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-cnn-over-cifar-10">
+<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>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li>
+</ol>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa-installation">
+<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="data-preparation">
+<h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3>
+<p>The binary Cifar-10 dataset could be downloaded by</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="nb">bin</span>
+</pre></div>
+</div>
+<p>The Python version could be downloaded by</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="n">py</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="training">
+<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 âcifar-10-batches-pyâ folder.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <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 âvggâ to âalexnetâ, âresnetâ or âcaffeâ,
+where âcaffeâ refers to the alexnet model converted from Caffe. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <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 notranslate"><div class="highlight"><pre><span></span> <span class="o">./</span><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 notranslate"><div class="highlight"><pre><span></span> <span class="o">./</span><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 trains 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">
+<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 notranslate"><div class="highlight"><pre><span></span> <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; It returns the topk labels for each instance.</p>
+<p>The predict.py fileâs main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The âmodel.binâ file generated by the training program should be placed at the cifar10 folder to run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
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+ <li>Train AlexNet over ImageNet</li>
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+ 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 notranslate"><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 notranslate"><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 notranslate"><span class="pre">test.bin</span></code>), a mean file(<code class="docutils literal notranslate"><span class="pre">mean.bin</span></code>) and
+several training files(<code class="docutils literal notranslate"><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 notranslate"><span class="pre">create_data.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">-trainlist</span> <span class="pre"><file></span></code>: the file of training list;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-trainfolder</span> <span class="pre"><folder></span></code>: the folder of training images;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-testlist</span> <span class="pre"><file></span></code>: the file of test list;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-testfolder</span> <span class="pre"><floder></span></code>: the folder of test images;</li>
+<li><code class="docutils literal notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><span class="pre">run.sh</span></code>.</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><span class="pre">filesize</span></code> in data preprocessing;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-ntrain</span> <span class="pre"><int></span></code>: number of training images;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-ntest</span> <span class="pre"><int></span></code>: number of test images;</li>
+<li><code class="docutils literal notranslate"><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 notranslate"><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 notranslate"><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>name: DenseNet models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</li>
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+<div class="section" id="name-densenet-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-densenet-py">
+<h1>name: DenseNet models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py<a class="headerlink" href="#name-densenet-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-densenet-py" title="Permalink to this headline">¶</a></h1>
+</div>
+<div class="section" id="image-classification-using-densenet">
+<h1>Image Classification using DenseNet<a class="headerlink" href="#image-classification-using-densenet" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we convert DenseNet on <a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py">PyTorch</a>
+to SINGA for image classification.</p>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz
+ $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
+ $ tar xvf densenet-121.tar.gz
+</pre></div>
+</div>
+</li>
+<li><p class="first">Usage</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h
+</pre></div>
+</div>
+</li>
+<li><p class="first">Example</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # use cpu
+ $ python serve.py --use_cpu --parameter_file densenet-121.pickle --depth 121 &
+ # use gpu
+ $ python serve.py --parameter_file densenet-121.pickle --depth 121 &
+</pre></div>
+</div>
+<p>The parameter files for the following model and depth configuration pairs are provided:
+<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz">121</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-169.tar.gz">169</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-201.tar.gz">201</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-161.tar.gz">161</a></p>
+</li>
+<li><p class="first">Submit images for classification</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ 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">
+<h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
+<p>The parameter files were converted from the pytorch via the convert.py program.</p>
+<p>Usage:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py -h
+</pre></div>
+</div>
+</div>
+</div>
+
+
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+
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+ <li>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</li>
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+<div class="section" id="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">
+<h1>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<a class="headerlink" href="#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" title="Permalink to this headline">¶</a></h1>
+</div>
+<div class="section" id="image-classification-using-googlenet">
+<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">
+<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-default notranslate"><div class="highlight"><pre><span></span> $ 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-default notranslate"><div class="highlight"><pre><span></span> # 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-default notranslate"><div class="highlight"><pre><span></span> $ 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">
+<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">Caffeâs checkpoint file</a> into a pickle version
+After downloading the checkpoint file into <code class="docutils literal notranslate"><span class="pre">caffe_root/python</span></code> folder, run the following script</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><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="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">cPickle</span> <span class="k">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="nb">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 SINGAâs 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>
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--- incubator/singa/site/trunk/docs/model_zoo/examples/imagenet/inception/README.html (added)
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+<!DOCTYPE html>
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+ <title>name: Inception V4 on ImageNet SINGA version: 1.1.1 SINGA commit: parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 license: https://github.com/tensorflow/models/tree/master/slim — incubator-singa 2.0.0 documentation</title>
+
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+
+ <li>name: Inception V4 on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
+parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56
+license: https://github.com/tensorflow/models/tree/master/slim</li>
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+<div class="section" id="name-inception-v4-on-imagenet-singa-version-1-1-1-singa-commit-parameter-url-https-s3-ap-southeast-1-amazonaws-com-dlfile-inception-v4-tar-gz-parameter-sha1-5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56-license-https-github-com-tensorflow-models-tree-master-slim">
+<h1>name: Inception V4 on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
+parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56
+license: https://github.com/tensorflow/models/tree/master/slim<a class="headerlink" href="#name-inception-v4-on-imagenet-singa-version-1-1-1-singa-commit-parameter-url-https-s3-ap-southeast-1-amazonaws-com-dlfile-inception-v4-tar-gz-parameter-sha1-5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56-license-https-github-com-tensorflow-models-tree-master-slim" title="Permalink to this headline">¶</a></h1>
+</div>
+<div class="section" id="image-classification-using-inception-v4">
+<h1>Image Classification using Inception V4<a class="headerlink" href="#image-classification-using-inception-v4" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we convert Inception V4 trained on Tensorflow to SINGA for image classification.</p>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Download the parameter checkpoint file</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ wget
+ $ tar xvf inception_v4.tar.gz
+</pre></div>
+</div>
+</li>
+<li><p class="first">Download <a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh">synset_word.txt</a> file.</p>
+</li>
+<li><p class="first">Run the program</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # 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-default notranslate"><div class="highlight"><pre><span></span> $ 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">
+<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://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz">Tensorflowâs checkpoint file</a> into a pickle version.
+After downloading and decompressing the checkpoint file, run the following script</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py --file_name=inception_v4.ckpt
+</pre></div>
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