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svn commit: r1858059 [21/38] - in /incubator/singa/site/trunk: ./ en/
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Added: incubator/singa/site/trunk/en/docs/model_zoo/examples/index.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/model_zoo/examples/index.html?rev=1858059&view=auto
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--- incubator/singa/site/trunk/en/docs/model_zoo/examples/index.html (added)
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+<div class="toctree-wrapper compound">
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+<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="mnist/README.html">Train a RBM model against MNIST dataset</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="mnist/README.html#running-instructions">Running instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/alexnet/README.html">Train AlexNet over ImageNet</a><ul>
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+<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/densenet/README.html">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></li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="imagenet/densenet/README.html#details">Details</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/googlenet/README.html">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></li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="imagenet/googlenet/README.html#details">Details</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/inception/README.html">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
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+<li class="toctree-l1"><a class="reference internal" href="imagenet/resnet/README.html">name: Resnets on ImageNet
+SINGA version: 1.1
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+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
+license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="imagenet/resnet/README.html#details">Details</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="imagenet/vgg/README.html">name: VGG models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li>
+<li class="toctree-l1"><a class="reference internal" href="imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a><ul>
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Added: incubator/singa/site/trunk/en/docs/model_zoo/examples/mnist/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/model_zoo/examples/mnist/README.html?rev=1858059&view=auto
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--- incubator/singa/site/trunk/en/docs/model_zoo/examples/mnist/README.html (added)
+++ incubator/singa/site/trunk/en/docs/model_zoo/examples/mnist/README.html Wed Apr 24 14:57:35 2019
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+<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/%7Ehinton/science.pdf">Hintonâs paper</a></p>
+<div class="section" id="running-instructions">
+<h2>Running instructions<a class="headerlink" href="#running-instructions" title="Permalink to this headline">¶</a></h2>
+<ol>
+<li><p>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>Start the training</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">mnist</span><span class="o">.</span><span class="n">pkl</span><span class="o">.</span><span class="n">gz</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 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">mnist</span><span class="o">.</span><span class="n">pkl</span><span class="o">.</span><span class="n">gz</span> <span class="o">--</span><span class="n">use_gpu</span>
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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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+SINGA version: 1.1
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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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+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
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+
+ Unless required by applicable law or agreed to in writing,
+ 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 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><p>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.</p></li>
+<li><p>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>.</p></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>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>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><p><code class="docutils literal notranslate"><span class="pre">-trainlist</span> <span class="pre"><file></span></code>: the file of training list;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">-trainfolder</span> <span class="pre"><folder></span></code>: the folder of training images;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">-testlist</span> <span class="pre"><file></span></code>: the file of test list;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">-testfolder</span> <span class="pre"><floder></span></code>: the folder of test images;</p></li>
+<li><p><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;</p></li>
+<li><p><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.</p></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>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>You may change the parameters in <code class="docutils literal notranslate"><span class="pre">run.sh</span></code>.</p>
+<ul class="simple">
+<li><p><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;</p></li>
+<li><p><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>;</p></li>
+<li><p><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;</p></li>
+<li><p><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;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">-ntrain</span> <span class="pre"><int></span></code>: number of training images;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">-ntest</span> <span class="pre"><int></span></code>: number of test images;</p></li>
+<li><p><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;</p></li>
+<li><p><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);</p></li>
+<li><p><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.</p></li>
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Added: incubator/singa/site/trunk/en/docs/model_zoo/imagenet/densenet/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/model_zoo/imagenet/densenet/README.html?rev=1858059&view=auto
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--- incubator/singa/site/trunk/en/docs/model_zoo/imagenet/densenet/README.html (added)
+++ incubator/singa/site/trunk/en/docs/model_zoo/imagenet/densenet/README.html Wed Apr 24 14:57:35 2019
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">name: DenseNet models on ImageNet
+SINGA version: 1.1.1
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+license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#image-classification-using-densenet">Image Classification using DenseNet</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#details">Details</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../googlenet/README.html">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
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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>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>Usage</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h
+</pre></div>
+</div>
+</li>
+<li><p>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>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>
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