DeepDream - a code example for visualizing Neural Networks

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glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/blog/">Blog</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-8 glue-grid__col--span-7-md glue-grid__col--span-8-lg">DeepDream - a code example for visualizing Neural Networks</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-8 glue-grid__col--span-7-md glue-grid__col--span-8-lg"> <div class="basic-hero--blog-detail__description"><p>July 1, 2015</p><span class="dot-separator"></span><p>Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer</p></div> </div> <div class="bhoig__cta glue-grid__col--span-8 glue-grid__col--span-7-md glue-grid__col--span-8-lg"> </div> </div> </div> </section> <div class="glue-page"> <div class="glue-grid blog-detail-page__grid"> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="quicklinks-wrapper--mobile"> <div class="block-quick_links"> <section class="quicklinks"> <h2 class="eyebrow">Quick links</h2> <ul class="quicklinks__list"> <li class="quicklinks__item quicklinks__item--share js-quicklinks-share"> <button class="quicklinks__share-button js-quicklinks-share__button" aria-expanded="false" aria-controls="js-quicklinks-share__list"> <span class="icon 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In addition to gaining some insight on how these networks carry out classification tasks, we found that this process also generated some beautiful art.<br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-CdUrPm7x5Ig/VZQIGjJzP0I/AAAAAAAAAnI/qhqchfzdaOc/s1600/image00.jpg" target="_blank" rel="noopener noreferrer"><img border="0" height="640" src="https://1.bp.blogspot.com/-CdUrPm7x5Ig/VZQIGjJzP0I/AAAAAAAAAnI/qhqchfzdaOc/s640/image00.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Top: Input image. Bottom: output image made using a network trained on places by <a href="http://places.csail.mit.edu/" target="_blank" rel="noopener noreferrer">MIT Computer Science and AI Laboratory</a>.</td></tr></tbody></table>We have seen a lot of interest and received some great questions, from programmers and artists alike, about the details of how these visualizations are made. We have decided to open source the code we used to generate these images in an <a href="https://github.com/google/deepdream" target="_blank" rel="noopener noreferrer">IPython notebook</a>, so now you can make neural network inspired images yourself! <br /><br />The code is based on <a href="http://caffe.berkeleyvision.org/" target="_blank" rel="noopener noreferrer">Caffe</a> and uses available open source packages, and is designed to have as few dependencies as possible. To get started, you will need the following (full details in the notebook):<br /><br /><ul><li><a href="http://www.numpy.org/" target="_blank" rel="noopener noreferrer">NumPy</a>, <a href="http://www.scipy.org/" target="_blank" rel="noopener noreferrer">SciPy</a>, <a href="http://www.pythonware.com/products/pil/" target="_blank" rel="noopener noreferrer">PIL</a>, <a href="http://ipython.org/" target="_blank" rel="noopener noreferrer">IPython</a>, or a scientific python distribution such as <a href="http://continuum.io/downloads" target="_blank" rel="noopener noreferrer">Anaconda</a> or <a href="https://store.enthought.com/" target="_blank" rel="noopener noreferrer">Canopy</a>.</li><li><a href="http://caffe.berkeleyvision.org/" target="_blank" rel="noopener noreferrer">Caffe</a> deep learning framework (<a href="http://caffe.berkeleyvision.org/installation.html" target="_blank" rel="noopener noreferrer">Installation instructions</a>)</li></ul><br />Once you’re set up, you can supply an image and choose which layers in the network to enhance, how many iterations to apply and how far to zoom in. Alternatively, different pre-trained networks can be plugged in. <br /><br />It'll be interesting to see what imagery people are able to generate. 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