mhjensen
217 lines
9.4 KiB
HTML
217 lines
9.4 KiB
HTML
<!--
|
||
Automatically generated HTML file from DocOnce source
|
||
(https://github.com/hplgit/doconce/)
|
||
-->
|
||
<html>
|
||
<head>
|
||
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
|
||
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||
<meta name="description" content="Convolutional Neural Networks">
|
||
|
||
<title>Convolutional Neural Networks</title>
|
||
|
||
<!-- Bootstrap style: bootstrap -->
|
||
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
|
||
<!-- not necessary
|
||
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
|
||
-->
|
||
|
||
<style type="text/css">
|
||
|
||
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
|
||
.dropdown-menu {
|
||
height: auto;
|
||
max-height: 400px;
|
||
overflow-x: hidden;
|
||
}
|
||
|
||
/* Adds an invisible element before each target to offset for the navigation
|
||
bar */
|
||
.anchor::before {
|
||
content:"";
|
||
display:block;
|
||
height:50px; /* fixed header height for style bootstrap */
|
||
margin:-50px 0 0; /* negative fixed header height */
|
||
}
|
||
</style>
|
||
|
||
|
||
</head>
|
||
|
||
<!-- tocinfo
|
||
{'highest level': 2,
|
||
'sections': [('Convolutional Neural Networks (recognizing images)',
|
||
2,
|
||
None,
|
||
'___sec0'),
|
||
('Regular NNs don’t scale well to full images',
|
||
2,
|
||
None,
|
||
'___sec1'),
|
||
('3D volumes of neurons', 2, None, '___sec2'),
|
||
('Layers used to build CNNs', 2, None, '___sec3'),
|
||
('Transforming images', 2, None, '___sec4'),
|
||
('CNNs in brief', 2, None, '___sec5'),
|
||
('CNNs in more detail, building convolutional neural networks in '
|
||
'Tensorflow and Keras',
|
||
2,
|
||
None,
|
||
'___sec6'),
|
||
('Setting it up', 2, None, '___sec7'),
|
||
('The MNIST dataset again', 2, None, '___sec8'),
|
||
('Strong correlations', 2, None, '___sec9'),
|
||
('Layers of a CNN', 2, None, '___sec10'),
|
||
('Systematic reduction', 2, None, '___sec11'),
|
||
('Prerequisites: Collect and pre-process data',
|
||
2,
|
||
None,
|
||
'___sec12'),
|
||
('Importing Keras and Tensorflow', 2, None, '___sec13'),
|
||
('Using TensorFlow backend', 2, None, '___sec14'),
|
||
('Train the model', 2, None, '___sec15'),
|
||
('Visualizing the results', 2, None, '___sec16'),
|
||
('Running with Keras', 2, None, '___sec17'),
|
||
('Final part', 2, None, '___sec18'),
|
||
('Final visualization', 2, None, '___sec19'),
|
||
('Fun links', 2, None, '___sec20')]}
|
||
end of tocinfo -->
|
||
|
||
<body>
|
||
|
||
|
||
|
||
<script type="text/x-mathjax-config">
|
||
MathJax.Hub.Config({
|
||
TeX: {
|
||
equationNumbers: { autoNumber: "none" },
|
||
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
|
||
}
|
||
});
|
||
</script>
|
||
<script type="text/javascript" async
|
||
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
|
||
</script>
|
||
|
||
|
||
|
||
|
||
<!-- Bootstrap navigation bar -->
|
||
<div class="navbar navbar-default navbar-fixed-top">
|
||
<div class="navbar-header">
|
||
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
|
||
<span class="icon-bar"></span>
|
||
<span class="icon-bar"></span>
|
||
<span class="icon-bar"></span>
|
||
</button>
|
||
<a class="navbar-brand" href="cnn-bs.html">Convolutional Neural Networks</a>
|
||
</div>
|
||
|
||
<div class="navbar-collapse collapse navbar-responsive-collapse">
|
||
<ul class="nav navbar-nav navbar-right">
|
||
<li class="dropdown">
|
||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||
<ul class="dropdown-menu">
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs001.html#___sec0" style="font-size: 80%;">Convolutional Neural Networks (recognizing images)</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs002.html#___sec1" style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs003.html#___sec2" style="font-size: 80%;">3D volumes of neurons</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs004.html#___sec3" style="font-size: 80%;">Layers used to build CNNs</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs005.html#___sec4" style="font-size: 80%;">Transforming images</a></li>
|
||
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">CNNs in brief</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs007.html#___sec6" style="font-size: 80%;">CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs008.html#___sec7" style="font-size: 80%;">Setting it up</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs009.html#___sec8" style="font-size: 80%;">The MNIST dataset again</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs010.html#___sec9" style="font-size: 80%;">Strong correlations</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs011.html#___sec10" style="font-size: 80%;">Layers of a CNN</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs012.html#___sec11" style="font-size: 80%;">Systematic reduction</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs013.html#___sec12" style="font-size: 80%;">Prerequisites: Collect and pre-process data</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs014.html#___sec13" style="font-size: 80%;">Importing Keras and Tensorflow</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs015.html#___sec14" style="font-size: 80%;">Using TensorFlow backend</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs016.html#___sec15" style="font-size: 80%;">Train the model</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs017.html#___sec16" style="font-size: 80%;">Visualizing the results</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs018.html#___sec17" style="font-size: 80%;">Running with Keras</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs019.html#___sec18" style="font-size: 80%;">Final part</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs020.html#___sec19" style="font-size: 80%;">Final visualization</a></li>
|
||
<!-- navigation toc: --> <li><a href="._cnn-bs021.html#___sec20" style="font-size: 80%;">Fun links</a></li>
|
||
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</div> <!-- end of navigation bar -->
|
||
|
||
<div class="container">
|
||
|
||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||
|
||
<a name="part0006"></a>
|
||
<!-- !split -->
|
||
|
||
<h2 id="___sec5" class="anchor">CNNs in brief </h2>
|
||
|
||
<p>
|
||
In summary:
|
||
|
||
<ul>
|
||
<li> A CNN architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)</li>
|
||
<li> There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)</li>
|
||
<li> Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function</li>
|
||
<li> Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)</li>
|
||
<li> Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)</li>
|
||
</ul>
|
||
|
||
For more material on convolutional networks, we strongly recommend
|
||
the course
|
||
<a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" target="_self">IN5400 – Machine Learning for Image Analysis</a>
|
||
and the slides of <a href="http://cs231n.github.io/convolutional-networks/" target="_self">CS231</a> which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). <a href="http://neuralnetworksanddeeplearning.com/chap6.html" target="_self">Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs</a>.
|
||
|
||
<p>
|
||
<p>
|
||
<!-- navigation buttons at the bottom of the page -->
|
||
<ul class="pagination">
|
||
<li><a href="._cnn-bs005.html">«</a></li>
|
||
<li><a href="._cnn-bs000.html">1</a></li>
|
||
<li><a href="._cnn-bs001.html">2</a></li>
|
||
<li><a href="._cnn-bs002.html">3</a></li>
|
||
<li><a href="._cnn-bs003.html">4</a></li>
|
||
<li><a href="._cnn-bs004.html">5</a></li>
|
||
<li><a href="._cnn-bs005.html">6</a></li>
|
||
<li class="active"><a href="._cnn-bs006.html">7</a></li>
|
||
<li><a href="._cnn-bs007.html">8</a></li>
|
||
<li><a href="._cnn-bs008.html">9</a></li>
|
||
<li><a href="._cnn-bs009.html">10</a></li>
|
||
<li><a href="._cnn-bs010.html">11</a></li>
|
||
<li><a href="._cnn-bs011.html">12</a></li>
|
||
<li><a href="._cnn-bs012.html">13</a></li>
|
||
<li><a href="._cnn-bs013.html">14</a></li>
|
||
<li><a href="._cnn-bs014.html">15</a></li>
|
||
<li><a href="._cnn-bs015.html">16</a></li>
|
||
<li><a href="">...</a></li>
|
||
<li><a href="._cnn-bs021.html">22</a></li>
|
||
<li><a href="._cnn-bs007.html">»</a></li>
|
||
</ul>
|
||
<!-- ------------------- end of main content --------------- -->
|
||
|
||
</div> <!-- end container -->
|
||
<!-- include javascript, jQuery *first* -->
|
||
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
|
||
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
|
||
|
||
<!-- Bootstrap footer
|
||
<footer>
|
||
<a href="http://..."><img width="250" align=right src="http://..."></a>
|
||
</footer>
|
||
-->
|
||
|
||
|
||
<center style="font-size:80%">
|
||
<!-- copyright only on the titlepage -->
|
||
</center>
|
||
|
||
|
||
</body>
|
||
</html>
|
||
|
||
|