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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.NeuralNetwork-module.html">Package NeuralNetwork</a> ::
<a href="Bio.NeuralNetwork.BackPropagation-module.html">Package BackPropagation</a> ::
<a href="Bio.NeuralNetwork.BackPropagation.Network-module.html">Module Network</a> ::
Class BasicNetwork
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class BasicNetwork</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.NeuralNetwork.BackPropagation.Network-pysrc.html#BasicNetwork">source code</a></span></p>
<p>Represent a Basic Neural Network with three layers.</p>
<p>This deals with a Neural Network containing three layers:</p>
<p>o Input Layer</p>
<p>o Hidden Layer</p>
<p>o Output Layer</p>
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<td><span class="summary-sig"><a name="__init__"></a><span class="summary-sig-name">__init__</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">input_layer</span>,
<span class="summary-sig-arg">hidden_layer</span>,
<span class="summary-sig-arg">output_layer</span>)</span><br />
Initialize the network with the three layers.</td>
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<td><span class="summary-sig"><a href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#train" class="summary-sig-name">train</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">training_examples</span>,
<span class="summary-sig-arg">validation_examples</span>,
<span class="summary-sig-arg">stopping_criteria</span>,
<span class="summary-sig-arg">learning_rate</span>,
<span class="summary-sig-arg">momentum</span>)</span><br />
Train the neural network to recognize particular examples.</td>
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<span class="codelink"><a href="Bio.NeuralNetwork.BackPropagation.Network-pysrc.html#BasicNetwork.train">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.NeuralNetwork.BackPropagation.Network.BasicNetwork-class.html#predict" class="summary-sig-name">predict</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">inputs</span>)</span><br />
Predict outputs from the neural network with the given inputs.</td>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">train</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">training_examples</span>,
<span class="sig-arg">validation_examples</span>,
<span class="sig-arg">stopping_criteria</span>,
<span class="sig-arg">learning_rate</span>,
<span class="sig-arg">momentum</span>)</span>
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<p>Train the neural network to recognize particular examples.</p>
<p>Arguments:</p>
<p>o training_examples -- A list of TrainingExample classes that will be
used to train the network.</p>
<p>o validation_examples -- A list of TrainingExample classes that are
used to validate the network as it is trained. These examples are not
used to train so the provide an independent method of checking how the
training is doing. Normally, when the error from these examples starts to
rise, then it's time to stop training.</p>
<p>o stopping_criteria -- A function, that when passed the number of
iterations, the training error, and the validation error, will determine
when to stop learning.</p>
<p>o learning_rate -- The learning rate of the neural network.</p>
<p>o momentum -- The momentum of the NN, which describes how much of the
prevoious weight change to use.</p>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">predict</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">inputs</span>)</span>
</h3>
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><span class="codelink"><a href="Bio.NeuralNetwork.BackPropagation.Network-pysrc.html#BasicNetwork.predict">source code</a></span>
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<p>Predict outputs from the neural network with the given inputs.</p>
<p>This uses the current neural network to predict outputs, no training
of the neural network is done here.</p>
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