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<!doctype html>
<!-- Copyright 2016 Google Inc. All Rights Reserved.
Licensed 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.
==============================================================================-->
<html>
<head lang="en">
<link rel="icon" type="image/png" href="favicon.png">
<meta charset="utf-8">
<meta name="viewport" content="width=1024">
<meta name="keywords" content="neural networks,machine learning,javascript">
<meta property="og:type" content="article"/>
<meta property="og:title" content="Tensorflow — Neural Network Playground"/>
<meta property="og:description" content="Tinker with a real neural network right here in your browser.">
<meta property="og:url" content="http://playground.tensorflow.org"/>
<meta property="og:image" content="http://playground.tensorflow.org/preview.png"/>
<meta name="twitter:card" value="summary_large_image">
<meta name="twitter:title" content="Tensorflow — Neural Network Playground">
<meta name="twitter:description" content="Tinker with a real neural network right here in your browser.">
<meta name="twitter:url" content="http://playground.tensorflow.org">
<meta name="twitter:image" content="http://playground.tensorflow.org/preview.png">
<meta name="twitter:image:width" content="560">
<meta name="twitter:image:height" content="295">
<meta name="author" content="Daniel Smilkov and Shan Carter">
<title>A Neural Network Playground</title>
<link rel="stylesheet" href="bundle.css" type="text/css">
<link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500|Material+Icons" rel="stylesheet" type="text/css">
<script src="lib.js"></script>
</head>
<body>
<!-- GitHub link -->
<a class="github-link" href="https://github.com/tensorflow/playground" title="Source on GitHub">
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 60.5 60.5" width="60" height="60">
<polygon class="bg" points="60.5,60.5 0,0 60.5,0 "/>
<path class="icon" d="M43.1,5.8c-6.6,0-12,5.4-12,12c0,5.3,3.4,9.8,8.2,11.4c0.6,0.1,0.8-0.3,0.8-0.6c0-0.3,0-1,0-2c-3.3,0.7-4-1.6-4-1.6c-0.5-1.4-1.3-1.8-1.3-1.8c-1.1-0.7,0.1-0.7,0.1-0.7c1.2,0.1,1.8,1.2,1.8,1.2c1.1,1.8,2.8,1.3,3.5,1c0.1-0.8,0.4-1.3,0.8-1.6c-2.7-0.3-5.5-1.3-5.5-5.9c0-1.3,0.5-2.4,1.2-3.2c-0.1-0.3-0.5-1.5,0.1-3.2c0,0,1-0.3,3.3,1.2c1-0.3,2-0.4,3-0.4c1,0,2,0.1,3,0.4c2.3-1.6,3.3-1.2,3.3-1.2c0.7,1.7,0.2,2.9,0.1,3.2c0.8,0.8,1.2,1.9,1.2,3.2c0,4.6-2.8,5.6-5.5,5.9c0.4,0.4,0.8,1.1,0.8,2.2c0,1.6,0,2.9,0,3.3c0,0.3,0.2,0.7,0.8,0.6c4.8-1.6,8.2-6.1,8.2-11.4C55.1,11.2,49.7,5.8,43.1,5.8z"/>
</svg>
</a>
<!-- Header -->
<header>
<h1 class="l--page">Tinker With a <b>Neural Network</b> <span class="optional">Right Here </span>in Your Browser.<br>Don’t Worry, You Can’t Break It. We Promise.</h1>
</header>
<!-- Top Controls -->
<div id="top-controls">
<div class="container l--page">
<div class="timeline-controls">
<button class="mdl-button mdl-js-button mdl-button--icon" id="reset-button" title="Reset the network">
<i class="material-icons">replay</i>
</button>
<button class="mdl-button mdl-js-button mdl-button--fab mdl-button--colored ui-playButton" id="play-pause-button" title="Run/Pause">
<i class="material-icons">play_arrow</i>
<i class="material-icons">pause</i>
</button>
<button class="mdl-button mdl-js-button mdl-button--icon" id="next-step-button" title="Step">
<i class="material-icons">skip_next</i>
</button>
</div>
<div class="control">
<span class="label">Iterations</span>
<span class="value" id="iter-number"></span>
</div>
<div class="control ui-learningRate">
<label for="learningRate">Learning rate</label>
<div class="select">
<select id="learningRate">
<option value="0.00001">0.00001</option>
<option value="0.0001">0.0001</option>
<option value="0.001">0.001</option>
<option value="0.003">0.003</option>
<option value="0.01">0.01</option>
<option value="0.03">0.03</option>
<option value="0.1">0.1</option>
<option value="0.3">0.3</option>
<option value="1">1</option>
<option value="3">3</option>
<option value="10">10</option>
</select>
</div>
</div>
<div class="control ui-activation">
<label for="activations">Activation</label>
<div class="select">
<select id="activations">
<option value="relu">ReLU</option>
<option value="tanh">Tanh</option>
<option value="sigmoid">Sigmoid</option>
<option value="linear">Linear</option>
</select>
</div>
</div>
<div class="control ui-regularization">
<label for="regularizations">Regularization</label>
<div class="select">
<select id="regularizations">
<option value="none">None</option>
<option value="L1">L1</option>
<option value="L2">L2</option>
</select>
</div>
</div>
<div class="control ui-regularizationRate">
<label for="regularRate">Regularization rate</label>
<div class="select">
<select id="regularRate">
<option value="0">0</option>
<option value="0.001">0.001</option>
<option value="0.003">0.003</option>
<option value="0.01">0.01</option>
<option value="0.03">0.03</option>
<option value="0.1">0.1</option>
<option value="0.3">0.3</option>
<option value="1">1</option>
<option value="3">3</option>
<option value="10">10</option>
</select>
</div>
</div>
<div class="control ui-problem">
<label for="problem">Problem type</label>
<div class="select">
<select id="problem">
<option value="classification">Classification</option>
<option value="regression">Regression</option>
</select>
</div>
</div>
</div>
</div>
<!-- Main Part -->
<div id="main-part" class="l--page">
<!-- Data Column-->
<div class="column data">
<h4>
<span>Data</span>
</h4>
<div class="ui-dataset">
<p>Which dataset do you want to use?</p>
<div class="dataset-list">
<div class="dataset" title="Circle">
<canvas class="data-thumbnail" data-dataset="circle"></canvas>
</div>
<div class="dataset" title="Exclusive or">
<canvas class="data-thumbnail" data-dataset="xor"></canvas>
</div>
<div class="dataset" title="Gaussian">
<canvas class="data-thumbnail" data-dataset="gauss"></canvas>
</div>
<div class="dataset" title="Spiral">
<canvas class="data-thumbnail" data-dataset="spiral"></canvas>
</div>
<div class="dataset" title="Plane">
<canvas class="data-thumbnail" data-regDataset="reg-plane"></canvas>
</div>
<div class="dataset" title="Multi gaussian">
<canvas class="data-thumbnail" data-regDataset="reg-gauss"></canvas>
</div>
</div>
</div>
<div>
<div class="ui-percTrainData">
<label for="percTrainData">Ratio of training to test data: <span class="value">XX</span>%</label>
<p class="slider">
<input class="mdl-slider mdl-js-slider" type="range" id="percTrainData" min="10" max="90" step="10">
</p>
</div>
<div class="ui-noise">
<label for="noise">Noise: <span class="value">XX</span></label>
<p class="slider">
<input class="mdl-slider mdl-js-slider" type="range" id="noise" min="0" max="50" step="5">
</p>
</div>
<div class="ui-batchSize">
<label for="batchSize">Batch size: <span class="value">XX</span></label>
<p class="slider">
<input class="mdl-slider mdl-js-slider" type="range" id="batchSize" min="1" max="30" step="1">
</p>
</div>
<button class="basic-button" id="data-regen-button" title="Regenerate data">
Regenerate
</button>
</div>
</div>
<!-- Features Column -->
<div class="column features">
<h4>Input</h4>
<p>Which properties do you want to feed in?</p>
<div id="network">
<svg id="svg" width="510" height="450">
<defs>
<marker id="markerArrow" markerWidth="7" markerHeight="13" refX="1" refY="6" orient="auto" markerUnits="userSpaceOnUse">
<path d="M2,11 L7,6 L2,2" />
</marker>
</defs>
</svg>
<div class="callout thumbnail">
<svg viewBox="0 0 30 30">
<defs>
<marker id="arrow" markerWidth="5" markerHeight="5" refx="5" refy="2.5" orient="auto" markerUnits="userSpaceOnUse">
<path d="M0,0 L5,2.5 L0,5 z"/>
</marker>
</defs>
<path d="M12,30C5,20 2,15 12,0" marker-end="url(#arrow)">
</svg>
<div class="label">
This is the output from one <b>neuron</b>. Hover to see it larger.
</div>
</div>
<div class="callout weights">
<svg viewBox="0 0 30 30">
<defs>
<marker id="arrow" markerWidth="5" markerHeight="5" refx="5" refy="2.5" orient="auto" markerUnits="userSpaceOnUse">
<path d="M0,0 L5,2.5 L0,5 z"/>
</marker>
</defs>
<path d="M12,30C5,20 2,15 12,0" marker-end="url(#arrow)">
</svg>
<div class="label">
The outputs are mixed with varying <b>weights</b>, shown by the thickness of the lines.
</div>
</div>
</div>
</div>
<!-- Hidden Layers Column -->
<div class="column hidden-layers">
<h4>
<div class="ui-numHiddenLayers">
<button id="add-layers" class="mdl-button mdl-js-button mdl-button--icon">
<i class="material-icons">add</i>
</button>
<button id="remove-layers" class="mdl-button mdl-js-button mdl-button--icon">
<i class="material-icons">remove</i>
</button>
</div>
<span id="num-layers"></span>
<span id="layers-label"></span>
</h4>
<div class="bracket"></div>
</div>
<!-- Output Column -->
<div class="column output">
<h4>Output</h4>
<div class="metrics">
<div class="output-stats ui-percTrainData">
<span>Test loss</span>
<div class="value" id="loss-test"></div>
</div>
<div class="output-stats train">
<span>Training loss</span>
<div class="value" id="loss-train"></div>
</div>
<div id="linechart"></div>
</div>
<div id="heatmap"></div>
<div style="float:left;margin-top:20px">
<div style="display:flex; align-items:center;">
<!-- Gradient color scale -->
<div class="label" style="width:105px; margin-right: 10px">
Colors shows data, neuron and weight values.
</div>
<svg width="150" height="30" id="colormap">
<defs>
<linearGradient id="gradient" x1="0%" y1="100%" x2="100%" y2="100%">
<stop offset="0%" stop-color="#f59322" stop-opacity="1"></stop>
<stop offset="50%" stop-color="#e8eaeb" stop-opacity="1"></stop>
<stop offset="100%" stop-color="#0877bd" stop-opacity="1"></stop>
</linearGradient>
</defs>
<g class="core" transform="translate(3, 0)">
<rect width="144" height="10" style="fill: url('#gradient');"></rect>
</g>
</svg>
</div>
<br/>
<div style="display:flex;">
<label class="ui-showTestData mdl-checkbox mdl-js-checkbox mdl-js-ripple-effect" for="show-test-data">
<input type="checkbox" id="show-test-data" class="mdl-checkbox__input" checked>
<span class="mdl-checkbox__label label">Show test data</span>
</label>
<label class="ui-discretize mdl-checkbox mdl-js-checkbox mdl-js-ripple-effect" for="discretize">
<input type="checkbox" id="discretize" class="mdl-checkbox__input" checked>
<span class="mdl-checkbox__label label">Discretize output</span>
</label>
</div>
</div>
</div>
</div>
<!-- More -->
<div class="more">
<!-- <button class="mdl-button mdl-js-button mdl-button--icon"><i class="material-icons">keyboard_arrow_down</i></button> -->
<button class="mdl-button mdl-js-button mdl-button--fab">
<i class="material-icons">keyboard_arrow_down</i>
</button>
</div>
<!-- Article -->
<article>
<div class="l--body">
<h2>Um, What Is a Neural Network?</h2>
<p>It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. For a more detailed introduction to neural networks, Michael Nielsen’s <a href="http://neuralnetworksanddeeplearning.com/index.html">Neural Networks and Deep Learning</a> is a good place to start. For more a more technical overview, try <a href="http://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
</div>
<div class="l--body">
<h2>This Is Cool, Can I Repurpose It?</h2>
<p>Please do! We’ve open sourced it on <a href="https://github.com/tensorflow/playground">GitHub</a> with the hope that it can make neural networks a little more accessible and easier to learn. You’re free to use it in any way that follows our <a href="https://github.com/tensorflow/playground/blob/master/LICENSE">Apache License</a>. And if you have any suggestions for additions or changes, please <a href="https://github.com/tensorflow/playground/issues">let us know</a>.</p>
<p>We’ve also provided some controls below to enable you tailor the playground to a specific topic or lesson. Just choose which features you’d like to be visible below then save <a class="hide-controls-link" href="#">this link</a>, or <a href="javascript:location.reload();">refresh</a> the page.</p>
<div class="hide-controls"></div>
</div>
<div class="l--body">
<h2>What Do All the Colors Mean?</h2>
<p>Orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values.</p>
<p>The data points (represented by small circles) are initially colored orange or blue, which correspond to positive one and negative one.</p>
<p>In the hidden layers, the lines are colored by the weights of the connections between neurons. Blue shows a positive weight, which means the network is using that output of the neuron as given. An orange line shows that the network is assiging a negative weight.</p>
<p>In the output layer, the dots are colored orange or blue depending on their original values. The background color shows what the network is predicting for a particular area. The intensity of the color shows how confident that prediction is.</p>
</div>
<div class="l--body">
<h2>Credits</h2>
<p>
This was created by Daniel Smilkov and Shan Carter.
This is a continuation of many people’s previous work — most notably Andrej Karpathy’s <a href="http://cs.stanford.edu/people/karpathy/convnetjs/">convnet.js</a>
and Chris Olah’s <a href="http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/">articles</a> about neural networks.
Many thanks also to D. Sculley for help with the original idea and to Fernanda Viégas and Martin Wattenberg and the rest of the
<a href="https://research.google.com/bigpicture/">Big Picture</a> and <a href="https://research.google.com/teams/brain/">Google Brain</a> teams for feedback and guidance.
</p>
</div>
</article>
<!-- Footer -->
<footer>
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<a href="https://www.tensorflow.org/" class="logo">
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