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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-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><p><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%.</p></li> +<li><p><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%.</p></li> +<li><p><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%.</p></li> +<li><p><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.</p></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>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>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>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>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> +</div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="../char-rnn/README.html" class="btn btn-neutral float-right" title="Train Char-RNN over plain text" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="../index.html" class="btn btn-neutral float-left" title="Model Zoo" 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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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="use-parameters-pre-trained-from-caffe-in-singa"> +<h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1> +<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p> +<div class="section" id="run-this-example"> +<h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2> +<p>You can run this example by simply executing <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code> +The script does the following work.</p> +<div class="section" id="obtain-the-caffe-model"> +<h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3> +<ul class="simple"> +<li><p>Download caffe model prototxt and parameter binary file.</p></li> +<li><p>Currently we only support the latest caffe format, if your model is in +previous version of caffe, please update it to current format.(This is +supported by caffe)</p></li> +<li><p>After updating, we can obtain two files, i.e., the prototxt and parameter +binary file.</p></li> +</ul> +</div> +<div class="section" id="prepare-test-images"> +<h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3> +<p>A few sample images are downloaded into the <code class="docutils literal notranslate"><span class="pre">test</span></code> folder.</p> +</div> +<div class="section" id="predict"> +<h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3> +<p>The <code class="docutils literal notranslate"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="n">model_txt</span> <span class="n">model_bin</span> <span class="n">imgclass</span> +</pre></div> +</div> +<p>where <code class="docutils literal notranslate"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models. +You can start the prediction program by executing the following command:</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> <span class="n">vgg16</span><span class="o">.</span><span class="n">prototxt</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">caffemodel</span> <span class="n">synset_words</span><span class="o">.</span><span class="n">txt</span> +</pre></div> +</div> +<p>Then you type in the image path, and the program would output the top-5 labels.</p> +<p>More Caffe models would be tested soon.</p> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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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-char-rnn-over-plain-text"> +<h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1> +<p>Recurrent neural networks (RNN) are widely used for modelling sequential data, +e.g., natural language sentences. This example describes how to implement a RNN +application (or model) using SINGAâs RNN layers. +We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn";>char-rnn</a> model as an +example, which trains over sentences or +source code, with each character as an input unit. Particularly, we will train +a RNN using GRU over Linux kernel source code. After training, we expect to +generate meaningful code from the model.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p></li> +<li><p>Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/";>kernel source code</a>. +Other plain text files can also be used.</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">linux_input</span><span class="o">.</span><span class="n">txt</span> +</pre></div> +</div> +<p>Some hyper-parameters could be set through command line,</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="o">-</span><span class="n">h</span> +</pre></div> +</div> +</li> +<li><p>Sample characters from the model by providing the number of characters to sample and the seed string.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="s1">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">'#include <std'</span> +</pre></div> +</div> +<p>Please replace âmodel.binâ with the path to one of the checkpoint paths.</p> +</li> +</ul> +</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. All other marks mentioned may be trademarks or registered trademarks of their respective owners. + + </p> + </div> + Built with <a href="http://sphinx-doc.org/";>Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme";>theme</a> provided by <a href="https://readthedocs.org";>Read the Docs</a>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.Navigation.enable(true); + }); + </script> + + + + + + +<div class="rst-versions" data-toggle="rst-versions" role="note" aria-label="versions"> + <span class="rst-current-version" data-toggle="rst-current-version"> + <span class="fa fa-book"> incubator-singa </span> + v: latest + <span class="fa fa-caret-down"></span> + </span> + <div class="rst-other-versions"> + <dl> + <dt>Languages</dt> + <dd><a href="../../../../../en/index.html">English</a></dd> + <dd><a href="../../../../../zh/index.html">ä¸æ</a></dd> + </dl> + <dl> + <dt>Versions</dt> + <dd><a href="http://singa.apache.org/v0.3.0/";>0.3</a></dd> + <dd><a href="http://singa.apache.org/v1.1.0/";>1.1</a></dd> + </dl> + + </div> + <a href="http://incubator.apache.org/";> <img src= "../../../../_static/apache.png" style="background-color:white;"> </a> + + <a href="https://github.com/apache/incubator-singa"; 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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-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><p><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%.</p></li> +<li><p><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%.</p></li> +<li><p><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%.</p></li> +<li><p><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.</p></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>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>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>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>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> +</div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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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-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> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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