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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + 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-a-rbm-model-against-mnist-dataset"> +<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> +</pre></div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 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. 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<li><a href="../../../index.html">Documentation</a> »</li> + + <li><a href="../../index.html">Model Zoo</a> »</li> + + <li>Train AlexNet over ImageNet</li> + + + <li class="wy-breadcrumbs-aside"> + + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + 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> +</ul> +</li> +</ul> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="../densenet/README.html" class="btn btn-neutral float-right" 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + 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. +--><hr class="docutils" /> +<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 [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="../googlenet/README.html" class="btn btn-neutral float-right" title="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"; accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="../alexnet/README.html" class="btn btn-neutral float-left" title="Train AlexNet over ImageNet" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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