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<!DOCTYPE html>
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<title>Network - Documentation</title>
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<li class="nav-link nav-home-link"><a href="index.html">Home</a></li><li class="nav-heading">Classes</li><li class="nav-heading"><span class="nav-item-type type-class">C</span><span class="nav-item-name"><a href="module-leaf-detective_main.Biases.html">Biases</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Biases.html#fillRandom">fillRandom</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Biases.html#populate">populate</a></span></li><li class="nav-heading"><span class="nav-item-type type-class">C</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html">Layer</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#addBiases">addBiases</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#addWeights">addWeights</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#apply">apply</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#connect">connect</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#populate">populate</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Layer.html#run">run</a></span></li><li class="nav-heading"><span class="nav-item-type type-class">C</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html">Network</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#addBiases">addBiases</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#addWeights">addWeights</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#backpropagate">backpropagate</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#calculateDeltas">calculateDeltas</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#connect">connect</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#feed">feed</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#populate">populate</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#predict">predict</a></span></li><li class="nav-item"><span class="nav-item-type type-function">F</span><span class="nav-item-name"><a href="module-leaf-detective_main.Network.html#run">run</a></span></li><li class="nav-heading"><span class="nav-item-type type-class">C</span><span class="nav-item-name"><a href="module-leaf-detective_main.Weights.html">Weights</a></span></li><li class="nav-item"><span 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<div id="main">
<h1 class="page-title">Network</h1>
<section>
<header>
<h2>
<span class="ancestors"><a href="module-leaf-detective_main.html">leaf-detective/main</a>.</span>
Network
</h2>
<div class="class-description"><p>Neural Network Class</p></div>
</header>
<article>
<div class="container-overview">
<div class="section-method">
<h2>Constructor</h2>
<h4 class="name" id="Network"><span class="type-signature"></span>new Network<span class="signature">(layers)</span><span class="type-signature"></span></h4>
<div class="description">
<p>Creates a neural network with given layers</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line248">line 248</a>
</li></ul></dd>
</dl>
<h5>Parameters:</h5>
<table class="params">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th class="last">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td class="name"><code>layers</code></td>
<td class="type">
<span class="param-type"><code>Array</code></span>
</td>
<td class="description last">
<p>array of Layer objects or numbers representing
layer length</p>
</td>
</tr>
</tbody>
</table>
<div class="section-examples">
<h5>Example</h5>
<pre class="prettyprint"><code>// Import Constructors and helpers
import { Network } from "lib/index.js";
import { gaussian } from "lib/activations.js";
// Create network
let network = new Network([2048, 1024, 512, 256], gaussian);
// Run network
network
.addWeights()
.addBiases()
.connect()
.run()</code></pre>
</div>
</div>
</div>
<h3 class="subsection-title">Extends</h3>
<ul>
<li>Array</li>
</ul>
<h3 class="subsection-title">Methods</h3>
<div class="section-method">
<h4 class="name" id="addBiases"><span class="type-signature"></span>addBiases<span class="signature">()</span><span class="type-signature"> → {Network}</span></h4>
<div class="description">
<p>Adds randomly filled bias matrix with correct dimensions to every layer
by calling Biases constructor</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line269">line 269</a>
</li></ul></dd>
</dl>
<div class="section-returns">
<h5>Returns:</h5>
<dl class="param-type">
<dt>
Type:
</dt>
<dd>
<span class="param-type"><code>Network</code></span>
</dd>
</dl>
</div>
</div>
<div class="section-method">
<h4 class="name" id="addWeights"><span class="type-signature"></span>addWeights<span class="signature">()</span><span class="type-signature"> → {Network}</span></h4>
<div class="description">
<p>Adds randomly filled weight matrix with correct dimensions to every layer
by calling Weights constructor</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line283">line 283</a>
</li></ul></dd>
</dl>
<div class="section-returns">
<h5>Returns:</h5>
<dl class="param-type">
<dt>
Type:
</dt>
<dd>
<span class="param-type"><code>Network</code></span>
</dd>
</dl>
</div>
</div>
<div class="section-method">
<h4 class="name" id="backpropagate"><span class="type-signature"></span>backpropagate<span class="signature">(targets, learningRate<span class="signature-attributes">opt</span>, momentum<span class="signature-attributes">opt</span>)</span><span class="type-signature"> → {Network}</span></h4>
<div class="description">
<p>Backpropagate input and adjust weights based on error</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line331">line 331</a>
</li></ul></dd>
</dl>
<h5>Parameters:</h5>
<table class="params">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Attributes</th>
<th>Default</th>
<th class="last">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td class="name"><code>targets</code></td>
<td class="type">
<span class="param-type"><code>Array</code></span>
</td>
<td class="attributes">
</td>
<td class="default">
</td>
<td class="description last">
<p>targets for the current dataset; must be as long
as output layer</p>
</td>
</tr>
<tr>
<td class="name"><code>learningRate</code></td>
<td class="type">
<span class="param-type"><code>Number</code></span>
</td>
<td class="attributes">
<optional><br>
</td>
<td class="default">
0.5
</td>
<td class="description last">
<p>learning rate; must be between 0 (excluded)
and 1 (included)</p>
</td>
</tr>
<tr>
<td class="name"><code>momentum</code></td>
<td class="type">
<span class="param-type"><code>Number</code></span>
</td>
<td class="attributes">
<optional><br>
</td>
<td class="default">
Math.random()
</td>
<td class="description last">
<p>momentum; must be between 0 (included) and 1
(excluded)</p>
</td>
</tr>
</tbody>
</table>
<div class="section-returns">
<h5>Returns:</h5>
<dl class="param-type">
<dt>
Type:
</dt>
<dd>
<span class="param-type"><code>Network</code></span>
</dd>
</dl>
</div>
<div class="section-examples">
<h5>Example</h5>
<pre class="prettyprint"><code>network
.run()
.backpropagate([1, 0]);</code></pre>
</div>
</div>
<div class="section-method">
<h4 class="name" id="calculateDeltas"><span class="type-signature"></span>calculateDeltas<span class="signature">(targets)</span><span class="type-signature"> → {Array}</span></h4>
<div class="description">
<p>Calculate deltas / "error term" for output layers and hidden layers</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line369">line 369</a>
</li></ul></dd>
</dl>
<h5>Parameters:</h5>
<table class="params">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th class="last">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td class="name"><code>targets</code></td>
<td class="type">
<span class="param-type"><code>Array</code></span>
</td>
<td class="description last">
<p>targets for the current dataset; must be as long
as output layer</p>
</td>
</tr>
</tbody>
</table>
<div class="section-returns">
<h5>Returns:</h5>
<dl class="param-type">
<dt>
Type:
</dt>
<dd>
<span class="param-type"><code>Array</code></span>
</dd>
</dl>
<div class="param-desc">
<p>array of delta-arrays with the width of the Network - 1
(every layer except input layer); every delta-array has the height of the
corresponding layer (number of neurons in that layer)</p>
</div>
</div>
</div>
<div class="section-method">
<h4 class="name" id="connect"><span class="type-signature"></span>connect<span class="signature">()</span><span class="type-signature"> → {Network}</span></h4>
<div class="description">
<p>Connects every layers with its succedding layer by calling layer.connect</p>
</div>
<dl class="details">
<dt class="tag-source">Source:</dt>
<dd class="tag-source"><ul class="dummy"><li>
<a href="index.js.html">index.js</a>, <a href="index.js.html#line298">line 298</a>
</li></ul></dd>
</dl>
<div class="section-returns">
<h5>Returns:</h5>
<dl class="param-type">
<dt>
Type:
</dt>
<dd>
<span class="param-type"><code>Network</code></span>
</dd>
</dl>
</div>
</div>
<div class="section-method">
<h4 class="name" id="feed"><span class="type-signature"></span>feed<span class="signature">(data)</span><span class="type-signature"> → {Network}</span></h4>
<div class="description">
<p>Populates input layer with data (alias: <code>Network#populate</code>)</p>