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<h1 class="refname">fann_create_shortcut</h1>
<p class="verinfo">(PECL fann >= 1.0.0)</p><p class="refpurpose"><span class="refname">fann_create_shortcut</span> — <span class="dc-title">Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections</span></p>
</div>
<div class="refsect1 description" id="refsect1-function.fann-create-shortcut-description">
<h3 class="title">Description</h3>
<div class="methodsynopsis dc-description">
<span class="type"><span class="type reference">reference</span></span> <span class="methodname"><strong>fann_create_shortcut</strong></span>
( <span class="methodparam"><span class="type">int</span> <code class="parameter">$num_layers</code></span>
, <span class="methodparam"><span class="type">int</span> <code class="parameter">$num_neurons1</code></span>
, <span class="methodparam"><span class="type">int</span> <code class="parameter">$num_neurons2</code></span>
[, <span class="methodparam"><span class="type">int</span> <code class="parameter">$...</code></span>
] )</div>
<p class="para rdfs-comment">
Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
</p>
<p class="para">
Shortcut connections are connections that skip layers. A fully connected network with shortcut connections, is a network
where all neurons are connected to all neurons in later layers. Including direct connections from the input layer to the output layer.
</p>
</div>
<div class="refsect1 parameters" id="refsect1-function.fann-create-shortcut-parameters">
<h3 class="title">Parameters</h3>
<dl>
<dt>
<span class="term"><em><code class="parameter">num_layers</code></em></span>
<dd>
<p class="para">
The total number of layers including the input and the output layer.
</p>
</dd>
</dt>
<dt>
<span class="term"><em><code class="parameter">num_neurons1</code></em></span>
<dd>
<p class="para">
Number of neurons in the first layer.
</p>
</dd>
</dt>
<dt>
<span class="term"><em><code class="parameter">num_neurons2</code></em></span>
<dd>
<p class="para">
Number of neurons in the second layer.
</p>
</dd>
</dt>
<dt>
<span class="term"><em><code class="parameter">...</code></em></span>
<dd>
<p class="para">
Number of neurons in other layers.
</p>
</dd>
</dt>
</dl>
</div>
<div class="refsect1 returnvalues" id="refsect1-function.fann-create-shortcut-returnvalues">
<h3 class="title">Return Values</h3>
<p class="para">
Returns a neural network resource on success, or <strong><code>FALSE</code></strong> on error.
</p>
</div>
<div class="refsect1 seealso" id="refsect1-function.fann-create-shortcut-seealso">
<h3 class="title">See Also</h3>
<p class="para">
<ul class="simplelist">
<li class="member"> <span class="function"><a href="function.fann-create-shortcut-array.html" class="function" rel="rdfs-seeAlso">fann_create_shortcut_array()</a> - Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections</span></li>
<li class="member"> <span class="function"><a href="function.fann-create-sparse.html" class="function" rel="rdfs-seeAlso">fann_create_sparse()</a> - Creates a standard backpropagation neural network, which is not fully connected</span></li>
<li class="member"> <span class="function"><a href="function.fann-create-standard.html" class="function" rel="rdfs-seeAlso">fann_create_standard()</a> - Creates a standard fully connected backpropagation neural network</span></li>
</ul>
</p>
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