AutoKeras: An AutoML Library for Deep Learning

<html> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=UA-131826476-1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-131826476-1'); </script> <meta http-equiv="Content-type" content="text/html;charset=UTF-8">  <link rel="icon" href="/img/favicon.ico"> <link rel="icon" type="image/png" href="/img/favicon-16x16.png"> <link rel="icon" type="image/png" href="/img/favicon-32x32.png"> <title>AutoKeras: An AutoML Library for Deep Learning</title> <link rel="alternate" type="application/rss+xml" href="/jmlr.xml" title="JMLR RSS"> <link rel="stylesheet" type="text/css" href="/style.css"> <style type="text/css"> . {font-family:verdana,helvetica,sans-serif} a {text-decoration:none;color:#3030a0} #fixed { position: absolute; top: 0; left: 0; width: 8em; height: 100%; } body > #fixed { position: fixed; } #content { margin-top: 1em; margin-left: 10em; margin-right: 0.5em; } img.jmlr { width: 7em; } img.rss { width: 2em; } ul li { margin-bottom: 0.5em; } </style>  <script type="text/x-mathjax-config"> MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$']]}}); </script> <script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> </script>  <meta name="citation_title" content="AutoKeras: An AutoML Library for Deep Learning"> <meta name="citation_author" content="Haifeng Jin"> <meta name="citation_author" content="Fran莽ois Chollet"> <meta name="citation_author" content="Qingquan Song"> <meta name="citation_author" content="Xia Hu"> <meta name="citation_journal_title" content="Journal of Machine Learning Research"> <meta name="citation_volume" content="24"> <meta name="citation_issue" content="6"> <meta name="citation_firstpage" content="1"> <meta name="citation_lastpage" content="6"> <meta name="citation_pdf_url" content="http://jmlr.org/papers/volume24/20-1355/20-1355.pdf"> <meta name="citation_publication_date" content="2023"> <meta name="citation_public_url" content="http://jmlr.org/papers/v24/20-1355.html"> <meta name="citation_abstract_html_url" content="http://jmlr.org/papers/v24/20-1355.html"> <meta name="citation_issn" content="1533-7928"> </head> <body> <div id="fixed"> <br> <a align="right" href="/" target=_top><img align="right" class="jmlr" src="/img/jmlr.jpg" border="0"></a> <p><br><br> <p align="right"> <A href="/"> Home Page </A> <p align="right"> <A href="/papers"> Papers </A> <p align="right"> <A href="/author-info.html"> Submissions </A> <p align="right"> <A href="/news.html"> News </A>  <p align="right" > <A href="/editorial-board.html"> Editorial Board </A> <p align="right" > <A href="/special_issues/"> Special Issues </A> <p align="right"> <A href="/mloss">Open Source Software</A> <p align="right"> <A href="https://proceedings.mlr.press/"> Proceedings (PMLR)</A> <p align="right"> <A href="https://data.mlr.press/"> Data (DMLR) </A> <p align="right"> <A href="/tmlr"> Transactions (TMLR) </A> <p align="right"> <A href="/search-jmlr.html"> Search </A> <p align="right"> <A href="/stats.html">Statistics</A> <p align="right"> <A href="/manudb"> Login </A></p> <p align="right"> <A href="/faq.html">Frequently Asked Questions </A></p> <p align="right"> <A href="/contact.html"> Contact Us </A></p> <br><br> <p align="right"> <A href="/jmlr.xml"> <img src="/img/RSS.gif" class="rss" alt="RSS Feed"> </A> </div> <div id="content"> <h2> AutoKeras: An AutoML Library for Deep Learning </h2> <p><b><i>Haifeng Jin, Fran莽ois Chollet, Qingquan Song, Xia Hu</i></b>; 24(6):1−6, 2023.</p> <h3>Abstract</h3> <p class="abstract"> To use deep learning, one needs to be familiar with various software tools like TensorFlow or Keras, as well as various model architecture and optimization best practices. Despite recent progress in software usability, deep learning remains a highly specialized occupation. To enable people with limited machine learning and programming experience to adopt deep learning, we developed AutoKeras, an Automated Machine Learning (AutoML) library that automates the process of model selection and hyperparameter tuning. AutoKeras encapsulates the complex process of building and training deep neural networks into a very simple and accessible interface, which enables novice users to solve standard machine learning problems with a few lines of code. Designed with practical applications in mind, AutoKeras is built on top of Keras and TensorFlow, and all AutoKeras-created models can be easily exported and deployed with the help of the TensorFlow ecosystem tooling. </p> <font color="gray"><p>[abs]</font>[<a id="pdf" target="_blank" href="/papers/volume24/20-1355/20-1355.pdf">pdf</a>][<a id="bib" href="/papers/v24/20-1355.bib">bib</a>]        [<a href="https://github.com/keras-team/autokeras">code</a>] <table width="100%"> <tr> <td align="right"><font size="-1">&copy <a target="_top" href="https://www.jmlr.org">JMLR</a> 2023. (<a href="https://github.com/JmlrOrg/v24/tree/main/20-1355">edit</a>, <a href="http://jmlr.org/beta/papers/v24/20-1355.html">beta</a>) </td> </tr> </table> </div>  <a style="font-size: 0" rel="me" href="https://sigmoid.social/@jmlr">Mastodon</a> </body> </html>

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AutoKeras: An AutoML Library for Deep Learning