Keras: Deep Learning for humans

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta name="description" content="Keras documentation"> <meta name="author" content="Keras Team"> <title>Keras: Deep Learning for humans</title>  <link href="css/bootstrap.min.css" rel="stylesheet">  <link href="https://fonts.googleapis.com/css?family=Open+Sans:wght@300;400;600;800&display=swap" rel="stylesheet">  <link href="css/landing.css" rel="stylesheet">  <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-5DNGF4N'); </script> <script> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-175165319-128', 'auto'); ga('send', 'pageview'); </script>  </head> <body>  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5DNGF4N" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>   <header class="masthead smooth-white-bg text-center"> <div class="container"> <img src='img/logo.png' class='logo' /> <div class="row"> <div class="col-xl-8 mx-auto"> <h1 class="mb-5">Simple. Flexible. Powerful.</h1> <div class="row mx-auto"> <div class="col-md px-1"> <a href='/getting_started/' class="btn btn-block btn-lg btn-primary">Get started</a> </div> <div class="col-md px-1"> <a href='/api/' class="btn btn-block btn-lg btn-secondary">API docs</a> </div> <div class="col-md px-1"> <a href='/guides/' class="btn btn-block btn-lg btn-secondary">Guides</a> </div> <div class="col-md px-1"> <a href='/examples/' class="btn btn-block btn-lg btn-secondary">Examples</a> </div> </div> </div> </div> </header> <div class="masthead text-center smooth-black-bg" style="padding: 1em;"> <div class="row"> <div class="col-xl-8 mx-auto" id="announcement-box"> Keras is now available for<br> JAX, TensorFlow, and PyTorch!<br> <a href="/keras_3/" id="announcement-link">Read the Keras 3.0 release announcement</a> </div> </div> </div>  <section class="testimonials text-center smooth-white-bg"> <div class="container"> <div class="row"> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras is one of the key building blocks in YouTube Discovery's new modeling infrastructure. It brings a clear, consistent API and a common way of expressing modeling ideas to 8 teams across the major surfaces of YouTube recommendations." </p> <h5><span class="quote-name">Maciej Kula</span><br><span class="quote-title">Staff Software Engineer - Google</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras has tremendously simplified the development workflow of Waymo's ML practitioners, with the benefits of a significantly simplified API, standardized interface and behaviors, easily shareable model building components, and highly improved debuggability." </p> <h5><span class="quote-name">Yiming Chen</span><br><span class="quote-title">Senior Software Engineer - Waymo</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "The best thing you can say about any software library is that the abstractions it chooses feel completely natural, such that there is zero friction between thinking about what you want to do and thinking about how you want to code it. That's exactly what you get with Keras." </p> <h5><span class="quote-name">Matthew Carrigan</span><br><span class="quote-title">Machine Learning Engineer - Hugging Face</span></h5> </div> </div> </div> <div class="row"> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras allows us to prototype, research and deploy deep learning models in an intuitive and streamlined manner. The functional API makes code comprehensible and stylistic, allowing for effective knowledge transfer between scientists on my team." </p> <h5><span class="quote-name">Aiden Arnold, PhD</span><br><span class="quote-title">Lead Data Scientist - Rune Labs</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras has something for every user: easy customisability for the academic; out-of-the-box, performant models and pipelines for use by the industry, and readable, modular code for the student. Keras has made it very simple to quickly iterate over experiments without worrying about low-level details." </p> <h5><span class="quote-name">Abheesht Sharma</span><br><span class="quote-title">Research Scientist - Amazon</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras is the perfect abstraction layer to build and operationalize Deep Learning models. I've been using it since 2018 to develop and deploy models for some of the largest companies in the world [...] a combination of Keras, TensorFlow, and TFX has no rival." </p> <h5><span class="quote-name">Santiago L. Valdarrama</span><br><span class="quote-title">Machine Learning Consultant</span></h5> </div> </div> </div> <div class="row"> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "What I personally like the most about Keras (aside from its intuitive APIs), is the ease of transitioning from research to production. I can train a Keras model, convert it to TF Lite and deploy it to mobile & edge devices." </p> <h5><span class="quote-name">Margaret Maynard-Reid</span><br><span class="quote-title">Machine Learning Engineer</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras is that sweet spot where you get flexibility for research and consistency for deployment. Keras is to Deep Learning what Ubuntu is to Operating Systems." </p> <h5><span class="quote-name">Aakash Nain</span><br><span class="quote-title">Research Engineer</span></h5> </div> </div> <div class="col-lg-4"> <div class="testimonial-item mx-auto mb-5"> <p class="font-weight-light mb-0 quote-content"> "Keras's user-friendly design means it's easy to learn and easy to use [...] it allows for the rapid prototyping and deployment of models across a variety of platforms." </p> <h5><span class="quote-name">Gareth Collins</span><br><span class="quote-title">Machine Learning Engineer</span></h5> </div> </div> </div> </div> </section>  <section class="showcase"> <div class="container-fluid p-0"> <div class="row no-gutters bottom-border smooth-black-bg"> <div class="col-lg-6 order-lg-2 text-white showcase-img" style="background-image: url('img/showcase-superpower.png');"></div> <div class="col-lg-6 order-lg-1 my-auto showcase-text"> <h2>A superpower for developers.</h2> <p class="lead mb-0"> The purpose of Keras is to give an <b>unfair advantage</b> to any developer looking to ship Machine Learning-powered apps. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Your models run faster thanks to XLA compilation with JAX and TensorFlow, and are easier to deploy across every surface (server, mobile, browser, embedded) thanks to the serving components from the TensorFlow and PyTorch ecosystems, such as TF Serving, TorchServe, TF Lite, TF.js, and more. </p> </div> </div> <div class="row no-gutters bottom-border smooth-white-bg"> <div class="col-lg-6 text-white showcase-img" style="background-image: url('img/showcase-api-2.png');"></div> <div class="col-lg-6 my-auto showcase-text"> <h2>Deep learning for humans.</h2> <p class="lead mb-0"> Keras is an API designed for human beings, not machines. Keras follows best practices for <b>reducing cognitive load</b>: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. Keras also gives the highest priority to crafting great documentation and developer guides. </p> </div> </div> <div class="row no-gutters bottom-border smooth-black-bg"> <div class="col-lg-6 order-lg-2 text-white showcase-img" style="background-image: url('img/framework-optionality.png');"></div> <div class="col-lg-6 order-lg-1 my-auto showcase-text"> <h2>Unlock framework optionality.</h2> <p class="lead mb-0"> Keras works with JAX, TensorFlow, and PyTorch. It enables you to create models that can move across framework boundaries and that can benefit from the ecosystem of all three of these frameworks. </p> </div> </div> <div class="row no-gutters bottom-border smooth-white-bg"> <div class="col-lg-6 text-white showcase-img" style="background-image: url('img/showcase-tpu.jpg');"></div> <div class="col-lg-6 my-auto showcase-text"> <h2>Exascale machine learning.</h2> <p class="lead mb-0"> Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire <a href='https://cloud.google.com/tpu'>TPU pod</a>. It's not only possible; it's easy. </p> </div> </div> <div class="row no-gutters bottom-border smooth-black-bg"> <div class="col-lg-6 order-lg-2 text-white showcase-img" style="background-image: url('img/showcase-lhc.jpg');"></div> <div class="col-lg-6 order-lg-1 my-auto showcase-text"> <h2>State-of-the-art research.</h2> <p class="lead mb-0"> Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles. </p> </div> </div> </div> </section> </body> <footer> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

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Keras: Deep Learning for humans