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<p><a id='Neural-Network-Factory-1'></a></p>
<h1 id="neural-network-factory">Neural Network Factory</h1>
<p>Neural network factory provide convenient helper functions to define common neural networks.</p>
<p><a id='MXNet.mx.MLP-Tuple{Any,Any}' href='#MXNet.mx.MLP-Tuple{Any,Any}'>#</a>
<strong><code>MXNet.mx.MLP</code></strong> &mdash; <em>Method</em>.</p>
<pre><code class="julia">MLP(input, spec; hidden_activation = :relu, prefix)
</code></pre>
<p>Construct a multi-layer perceptron. A MLP is a multi-layer neural network with fully connected layers.</p>
<p><strong>Arguments:</strong></p>
<ul>
<li><code>input::SymbolicNode</code>: the input to the mlp.</li>
<li><code>spec</code>: the mlp specification, a list of hidden dimensions. For example, <code>[128, (512, :sigmoid), 10]</code>. The number in the list indicate the number of hidden units in each layer. A tuple could be used to specify the activation of each layer. Otherwise, the default activation will be used (except for the last layer).</li>
<li><code>hidden_activation::Symbol</code>: keyword argument, default <code>:relu</code>, indicating the default activation for hidden layers. The specification here could be overwritten by layer-wise specification in the <code>spec</code> argument. Also activation is not applied to the last, i.e. the prediction layer. See <a href="../ndarray/#MXNet.mx.Activation-Tuple{Vararg{NDArray,N} where N}"><code>Activation</code></a> for a list of supported activation types.</li>
<li><code>prefix</code>: keyword argument, default <code>gensym()</code>, used as the prefix to name the constructed layers.</li>
</ul>
<p>Returns the constructed MLP.</p>
<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/nn-factory.jl#L18-L40' class='documenter-source'>source</a><br></p>
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