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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 lang="en">
<head>
<link href="favicon.png" rel="icon" type="image/png">
<meta charset="utf-8">
<meta content="width=1024" name="viewport">
<meta content="neural networks,machine learning,javascript" name="keywords">
<meta content="article" property="og:type" />
<meta
content="Tensorflow — Neural Network Playground"
property="og:title"
/>
<meta
content="Tinker with a real neural network right here in your browser."
property="og:description"
>
<meta content="http://playground.tensorflow.org" property="og:url" />
<meta
content="http://playground.tensorflow.org/preview.png"
property="og:image"
/>
<meta name="twitter:card" value="summary_large_image">
<meta content="Tensorflow — Neural Network Playground" name="twitter:title">
<meta
content="Tinker with a real neural network right here in your browser."
name="twitter:description"
>
<meta content="http://playground.tensorflow.org" name="twitter:url">
<meta
content="http://playground.tensorflow.org/preview.png"
name="twitter:image"
>
<meta content="560" name="twitter:image:width">
<meta content="295" name="twitter:image:height">
<meta content="Daniel Smilkov and Shan Carter" name="author">
<title>A Neural Network Playground</title>
<link href="bundle.css" rel="stylesheet" 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/CooLearning/playground"
target="_blank"
title="Source on GitHub"
>
<svg
height="60"
viewBox="0 0 60.5 60.5"
width="60"
xmlns="http://www.w3.org/2000/svg"
xmlns:xlink="http://www.w3.org/1999/xlink"
>
<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 ui-resetButton"
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 ui-stepButton"
id="next-step-button"
title="Step"
>
<i class="material-icons">skip_next</i>
</button>
</div>
<div class="control">
<span class="label">Epoch</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">0</option>
<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 class="l--page" id="main-part">
<!-- 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"
id="percTrainData"
max="90"
min="10"
step="10"
type="range"
>
</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"
id="noise"
max="50"
min="0"
step="5"
type="range"
>
</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"
id="batchSize"
max="30"
min="1"
step="1"
type="range"
>
</p>
</div>
<button
class="basic-button"
id="data-regen-button"
title="Regenerate data"
>
Regenerate
</button>
</div>
</div>
<!-- Features Column -->
<div class="column features">
<h4>Features</h4>
<p>Which properties do you want to feed in?</p>
<div id="network">
<svg height="450" id="svg" width="510">
<defs>
<marker
id="markerArrow"
markerHeight="13"
markerUnits="userSpaceOnUse"
markerWidth="7"
orient="auto"
refX="1"
refY="6"
>
<path d="M2,11 L7,6 L2,2" />
</marker>
</defs>
</svg>
<!-- Hover card -->
<div id="hovercard">
<div style="font-size:10px">Click anywhere to edit.</div>
<div>
<span class="type">Weight/Bias</span>
is
<span class="value">0.2</span>
<span>
<label>
<input type="number" />
</label>
</span>
</div>
</div>
<div class="callout thumbnail">
<svg viewBox="0 0 30 30">
<defs>
<marker
id="arrow"
markerHeight="5"
markerUnits="userSpaceOnUse"
markerWidth="5"
orient="auto"
refX="5"
refY="2.5"
>
<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"
markerHeight="5"
markerUnits="userSpaceOnUse"
markerWidth="5"
orient="auto"
refX="5"
refY="2.5"
>
<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>
<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 height="30" id="colormap" width="150">
<defs>
<linearGradient
id="gradient"
x1="0%"
x2="100%"
y1="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
height="10"
style="fill: url('#gradient');"
width="144"
></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
checked
class="mdl-checkbox__input"
id="show-test-data"
type="checkbox"
>
<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
checked
class="mdl-checkbox__input"
id="discretize"
type="checkbox"
>
<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 id="article-text">
<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 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>What Library Are You Using?</h2>
<p>We wrote a tiny neural network
<a href="https://github.com/tensorflow/playground/blob/master/src/nn.ts">
library
</a>
that meets the demands of this educational visualization. For
real-world applications, consider the
<a href="https://www.tensorflow.org/">TensorFlow</a>
library.
</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/demo/classify2d.html"
>convnet.js demo
</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>
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