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| <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 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> |
| <h2><a class="anchor" aria-hidden="true" id="instructions"></a><a href="#instructions" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Instructions</h2> |
| <h3><a class="anchor" aria-hidden="true" id="compile-singa"></a><a href="#compile-singa" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Compile SINGA</h3> |
| <p>Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the options in CMakeLists.txt or run <code>ccmake ..</code> in <code>build/</code> folder.</p> |
| <p>We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)</p> |
| <h3><a class="anchor" aria-hidden="true" id="data-download"></a><a href="#data-download" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data download</h3> |
| <ul> |
| <li>Please refer to step1-3 on <a 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 href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh">get_ilsvrc_aux.sh</a> or from <a href="http://www.image-net.org/download-images">Imagenet</a>.</li> |
| </ul> |
| <h3><a class="anchor" aria-hidden="true" id="data-preprocessing"></a><a href="#data-preprocessing" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data preprocessing</h3> |
| <blockquote> |
| <p>Please <code>cd</code> to <code>singa/examples/imagenet/alexnet/</code> for the following commands</p> |
| </blockquote> |
| <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> |
| <pre><code class="hljs css language-shell">sh create_data.sh |
| </code></pre> |
| <p>The script will generate a test file(<code>test.bin</code>), a mean file(<code>mean.bin</code>) and several training files(<code>trainX.bin</code>) in the specified output folder.</p></li> |
| <li><p>You can also change the parameters in <code>create_data.sh</code>.</p> |
| <ul> |
| <li><code>-trainlist <file></code>: the file of training list;</li> |
| <li><code>-trainfolder <folder></code>: the folder of training images;</li> |
| <li><code>-testlist <file></code>: the file of test list;</li> |
| <li><code>-testfolder <floder></code>: the folder of test images;</li> |
| <li><code>-outdata <folder></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>-filesize <int></code>: number of training images that stores in each binary file.</li> |
| </ul></li> |
| </ul> |
| <h3><a class="anchor" aria-hidden="true" id="training"></a><a href="#training" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Training</h3> |
| <ul> |
| <li><p>After preparing data, you can run the following command to train the Alexnet model.</p> |
| <pre><code class="hljs css language-shell">sh run.sh |
| </code></pre></li> |
| <li><p>You may change the parameters in <code>run.sh</code>.</p> |
| <ul> |
| <li><code>-epoch <int></code>: number of epoch to be trained, default is 90;</li> |
| <li><code>-lr <float></code>: base learning rate, the learning rate will decrease each 20 epochs, more specifically, <code>lr = lr * exp(0.1 * (epoch / 20))</code>;</li> |
| <li><code>-batchsize <int></code>: batchsize, it should be changed regarding to your memory;</li> |
| <li><code>-filesize <int></code>: number of training images that stores in each binary file, it is the same as the <code>filesize</code> in data preprocessing;</li> |
| <li><code>-ntrain <int></code>: number of training images;</li> |
| <li><code>-ntest <int></code>: number of test images;</li> |
| <li><code>-data <folder></code>: the folder which stores the binary files, it is exactly the output folder in data preprocessing step;</li> |
| <li><code>-pfreq <int></code>: the frequency(in batch) of printing current model status(loss and accuracy);</li> |
| <li><code>-nthreads <int></code>: the number of threads to load data which feed to the model.</li> |
| </ul></li> |
| </ul> |
| </span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/2.0.0/model-zoo-rbm-mnist"><span class="arrow-prev">← </span><span>Previous</span></a><a class="docs-next button" href="/docs/2.0.0/model-zoo-imagenet-densenet"><span>Next</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#instructions">Instructions</a><ul class="toc-headings"><li><a href="#compile-singa">Compile SINGA</a></li><li><a href="#data-download">Data download</a></li><li><a href="#data-preprocessing">Data preprocessing</a></li><li><a href="#training">Training</a></li></ul></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><a href="/" class="nav-home"><img src="/img/singa-logo-square.png" alt="Apache SINGA" width="66" height="58"/></a><div><h5>Docs</h5><a href="/docs/installation">Getting Started</a><a href="/docs/device">Guides</a><a href="/en/#">API Reference (coming soon)</a><a href="/docs/model-zoo-cnn-cifar10">Model Zoo</a><a href="/docs/download-singa">Development</a></div><div><h5>Community</h5><a href="/en/users.html">User Showcase</a><a href="/docs/history-singa">SINGA History</a><a href="/docs/team-list">SINGA Team</a><a href="/news">SINGA News</a><a href="https://github.com/apache/singa-doc">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa-doc" data-count-href="/apache/singa/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star this project on GitHub">apache/singa-doc</a></div><div class="social"><a href="https://twitter.com/ApacheSINGA" class="twitter-follow-button">Follow @ApacheSINGA</a></div></div><div><h5>Apache Software Foundation</h5><a href="https://apache.org/" target="_blank" rel="noreferrer noopener">Foundation</a><a href="http://www.apache.org/licenses/" target="_blank" rel="noreferrer noopener">License</a><a href="http://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noreferrer noopener">Sponsorship</a><a href="http://www.apache.org/foundation/thanks.html" target="_blank" rel="noreferrer noopener">Thanks</a><a href="http://www.apache.org/events/current-event" target="_blank" rel="noreferrer noopener">Events</a><a href="http://www.apache.org/security/" target="_blank" rel="noreferrer noopener">Security</a></div></section><div style="width:100%;text-align:center"><a href="https://apache.org/" target="_blank" rel="noreferrer noopener" class="ApacheOpenSource"><img src="/img/asf_logo_wide.svg" alt="Apache Open Source"/></a><section class="copyright" style="max-width:60%;margin:0 auto">Copyright © 2020 |
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