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<title>Creates a standard fully connected backpropagation neural network</title>
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<h1 class="refname">fann_create_standard</h1>
<p class="verinfo">(PECL fann >= 1.0.0)</p><p class="refpurpose"><span class="refname">fann_create_standard</span> — <span class="dc-title">Creates a standard fully connected backpropagation neural network</span></p>
</div>
<div class="refsect1 description" id="refsect1-function.fann-create-standard-description">
<h3 class="title">Description</h3>
<div class="methodsynopsis dc-description">
<span class="type">resource</span> <span class="methodname"><strong>fann_create_standard</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 fully connected backpropagation neural network.
</p>
<p class="para">
There will be a bias neuron in each layer (except the output layer),
and this bias neuron will be connected to all neurons in the next layer.
When running the network, the bias nodes always emits 1.
</p>
<p class="para">
To destroy a neural network use the <span class="function"><a href="function.fann-destroy.html" class="function">fann_destroy()</a></span> function.
</p>
</div>
<div class="refsect1 parameters" id="refsect1-function.fann-create-standard-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-standard-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-standard-seealso">
<h3 class="title">See Also</h3>
<p class="para">
<ul class="simplelist">
<li class="member"> <span class="function"><a href="function.fann-create-standard-array.html" class="function" rel="rdfs-seeAlso">fann_create_standard_array()</a> - Creates a standard fully connected backpropagation neural network using an array of layer sizes</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-shortcut.html" class="function" rel="rdfs-seeAlso">fann_create_shortcut()</a> - Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections</span></li>
</ul>
</p>
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