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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>jsFlow</title>
<link rel="shortcut icon" type="image/x-icon" href="./img/favicon.ico" media="screen" />
<!-- bootstrap -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css">
<!-- Google fonts -->
<link href='http://fonts.googleapis.com/css?family=Roboto:400,300' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="style.css" />
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">
<script src="convnet_demo/convnet-min.js"></script>
<script src="convnet_demo/convnet_demo.js"></script>
<script>
function page_loaded() {
$('#convask').click(function () {
$("#explain").slideToggle('fast');
})
start_convnet_demo();
}
</script>
</head>
<body onload="page_loaded()">
<div id="header" style="text-align:center">
<h1><b>jsFlow demo: Convolutional Neural Network on CIFAR-10</b></h1>
</div>
<div id="teaser">
<div id="convnetvis"></div>
<span class="glyphicon glyphicon-question-sign" id="convask"></span>
<div id="demomsg">
*This network is running live in your browser
</div>
</div>
<div class="container">
<div id="explain">
The Convolutional Neural Network in this example is classifying images live in your browser using
Javascript, at about 10 milliseconds per image. It takes an input image and transforms it through a series
of functions into class probabilities at the end. The transformed representations in this visualization can
be loosely thought of as the activations of the neurons along the way. The parameters of this function are
learned with backpropagation on a dataset of (image, label) pairs. This particular network is classifying <a
href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10</a> images into one of 10 classes and was
trained with <a href="http://cs.stanford.edu/people/karpathy/convnetjs/">ConvNetJS</a>. Its exact
architecture is [conv-relu-conv-relu-pool]x3-fc-softmax, for a total of 17 layers and 7000 parameters. It
uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these
numbers mean.
</div>
</div>
<!-- jQuery and Boostrap -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
<!-- json 颜色显示 -->
<script src="//unpkg.com/prismjs/components/prism-python.js"></script>
<script src="//unpkg.com/docsify-copy-code"></script>
</body>
</html>