<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Train AlexNet over ImageNet · Apache SINGA</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="&lt;!--- 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 &quot;License&quot;); 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 &quot;AS IS&quot; 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.  --&gt;"/><meta name="docsearch:version" content="2.0.0"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="Train AlexNet over ImageNet · Apache SINGA"/><meta property="og:type" content="website"/><meta property="og:url" content="https://feynmandna.github.io/"/><meta property="og:description" content="&lt;!--- 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 &quot;License&quot;); 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 &quot;AS IS&quot; 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.  --&gt;"/><meta property="og:image" content="https://feynmandna.github.io/img/singa_twitter_banner.jpeg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://feynmandna.github.io/img/singa_twitter_banner.jpeg"/><link rel="shortcut icon" href="/img/favicon.ico"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/atom-one-dark.min.css"/><link rel="alternate" type="application/atom+xml" href="https://feynmandna.github.io/blog/atom.xml" title="Apache SINGA Blog ATOM Feed"/><link rel="alternate" type="application/rss+xml" href="https://feynmandna.github.io/blog/feed.xml" title="Apache SINGA Blog RSS Feed"/><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="https://unpkg.com/vanilla-back-to-top@7.1.14/dist/vanilla-back-to-top.min.js"></script><script>
        document.addEventListener('DOMContentLoaded', function() {
          addBackToTop(
            {"zIndex":100}
          )
        });
        </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>2.0.0</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-internal"><li class="siteNavGroupActive"><a href="/docs/2.0.0/installation" target="_self">Docs</a></li><li class=""><a href="/docs/2.0.0/source-repository" target="_self">Community</a></li><li class=""><a href="/blog/" target="_self">News</a></li><li class=""><a target="_self"></a></li><li class=""><a href="https://github.com/apache/singa-doc" target="_self">GitHub</a></li></ul></nav></div></header></div></div><div class="navPusher"><div class="docMainWrapper wrapper"><div class="docsNavContainer" id="docsNav"><nav class="toc"><div class="toggleNav"><section class="navWrapper wrapper"><div class="navBreadcrumb wrapper"><div class="navToggle" id="navToggler"><div class="hamburger-menu"><div class="line1"></div><div class="line2"></div><div class="line3"></div></div></div><h2><i>›</i><span>Model Zoo</span></h2><div class="tocToggler" id="tocToggler"><i class="icon-toc"></i></div></div><div class="navGroups"><div class="navGroup"><h3 class="navGroupCategoryTitle">Getting Started</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/2.0.0/installation">Installation</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/software-stack">Software Stack</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/benchmark-train">Benchmark for Distributed Training</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Guides</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/2.0.0/device">Device</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/tensor">Tensor</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/autograd">Autograd in SINGA</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Model Zoo</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-cnn-cifar10">Train CNN over Cifar-10</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-char-rnn">Train Char-RNN over plain text</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-rbm-mnist">Train a RBM model against MNIST dataset</a></li><li class="navListItem navListItemActive"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-alexnet">Train AlexNet over ImageNet</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-densenet">Image Classification using DenseNet</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-googlenet">Image Classification using GoogleNet</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-inception">Image Classification using Inception V4</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-resnet">Image Classification using Residual Networks</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/model-zoo-imagenet-vgg">Image Classification using VGG</a></li></ul></div><div class="navGroup"><h3 class="navGroupCategoryTitle">Development</h3><ul class=""><li class="navListItem"><a class="navItem" href="/docs/2.0.0/download-singa">Download SINGA</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/build">Build SINGA from Source</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/contribute-code">How to Contribute Code</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/contribute-docs">How to Contribute to Documentation</a></li><li class="navListItem"><a class="navItem" href="/docs/2.0.0/how-to-release">How to Prepare a Release</a></li></ul></div></div></section></div><script>
            var coll = document.getElementsByClassName('collapsible');
            var checkActiveCategory = true;
            for (var i = 0; i < coll.length; i++) {
              var links = coll[i].nextElementSibling.getElementsByTagName('*');
              if (checkActiveCategory){
                for (var j = 0; j < links.length; j++) {
                  if (links[j].classList.contains('navListItemActive')){
                    coll[i].nextElementSibling.classList.toggle('hide');
                    coll[i].childNodes[1].classList.toggle('rotate');
                    checkActiveCategory = false;
                    break;
                  }
                }
              }

              coll[i].addEventListener('click', function() {
                var arrow = this.childNodes[1];
                arrow.classList.toggle('rotate');
                var content = this.nextElementSibling;
                content.classList.toggle('hide');
              });
            }

            document.addEventListener('DOMContentLoaded', function() {
              createToggler('#navToggler', '#docsNav', 'docsSliderActive');
              createToggler('#tocToggler', 'body', 'tocActive');

              var headings = document.querySelector('.toc-headings');
              headings && headings.addEventListener('click', function(event) {
                var el = event.target;
                while(el !== headings){
                  if (el.tagName === 'A') {
                    document.body.classList.remove('tocActive');
                    break;
                  } else{
                    el = el.parentNode;
                  }
                }
              }, false);

              function createToggler(togglerSelector, targetSelector, className) {
                var toggler = document.querySelector(togglerSelector);
                var target = document.querySelector(targetSelector);

                if (!toggler) {
                  return;
                }

                toggler.onclick = function(event) {
                  event.preventDefault();

                  target.classList.toggle(className);
                };
              }
            });
        </script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"><a class="edit-page-link button" href="https://github.com/apache/singa-doc/blob/master/docs/model-zoo-imagenet-alexnet.md" target="_blank" rel="noreferrer noopener">Edit</a><h1 id="__docusaurus" class="postHeaderTitle">Train AlexNet over ImageNet</h1></header><article><div><span><!--- 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.  -->
<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 &lt;file&gt;</code>: the file of training list;</li>
<li><code>-trainfolder &lt;folder&gt;</code>: the folder of training images;</li>
<li><code>-testlist &lt;file&gt;</code>: the file of test list;</li>
<li><code>-testfolder &lt;floder&gt;</code>: the folder of test images;</li>
<li><code>-outdata &lt;folder&gt;</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 &lt;int&gt;</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 &lt;int&gt;</code>: number of epoch to be trained, default is 90;</li>
<li><code>-lr &lt;float&gt;</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 &lt;int&gt;</code>: batchsize, it should be changed regarding to your memory;</li>
<li><code>-filesize &lt;int&gt;</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 &lt;int&gt;</code>: number of training images;</li>
<li><code>-ntest &lt;int&gt;</code>: number of test images;</li>
<li><code>-data &lt;folder&gt;</code>: the folder which stores the binary files, it is exactly the output folder in data preprocessing step;</li>
<li><code>-pfreq &lt;int&gt;</code>: the frequency(in batch) of printing current model status(loss and accuracy);</li>
<li><code>-nthreads &lt;int&gt;</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>Train a RBM model against MNIST dataset</span></a><a class="docs-next button" href="/docs/2.0.0/model-zoo-imagenet-densenet"><span class="function-name-prevnext">Image Classification using DenseNet</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
   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.</section></div></footer></div><script>window.twttr=(function(d,s, id){var js,fjs=d.getElementsByTagName(s)[0],t=window.twttr||{};if(d.getElementById(id))return t;js=d.createElement(s);js.id=id;js.src='https://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js, fjs);t._e = [];t.ready = function(f) {t._e.push(f);};return t;}(document, 'script', 'twitter-wjs'));</script></body></html>