/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.pattern;
import java.util.ArrayList;
import java.util.List;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
/**
* Used to create feedforward neural networks. A feedforward network has an
* input and output layers separated by zero or more hidden layers. The
* feedforward neural network is one of the most common neural network patterns.
*
* @author jheaton
*
*/
public class FeedForwardPattern implements NeuralNetworkPattern {
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* The activation function.
*/
private ActivationFunction activationHidden;
/**
* The activation function.
*/
private ActivationFunction activationOutput;
/**
* The number of hidden neurons.
*/
private final List<Integer> hidden = new ArrayList<Integer>();
/**
* Add a hidden layer, with the specified number of neurons.
*
* @param count
* The number of neurons to add.
*/
public void addHiddenLayer(final int count) {
this.hidden.add(count);
}
/**
* Clear out any hidden neurons.
*/
public void clear() {
this.hidden.clear();
}
/**
* Generate the feedforward neural network.
*
* @return The feedforward neural network.
*/
public MLMethod generate() {
if( this.activationOutput==null )
this.activationOutput = this.activationHidden;
final Layer input = new BasicLayer(null, true,
this.inputNeurons);
final BasicNetwork result = new BasicNetwork();
result.addLayer(input);
for (final Integer count : this.hidden) {
final Layer hidden = new BasicLayer(this.activationHidden, true, count);
result.addLayer(hidden);
}
final Layer output = new BasicLayer(this.activationOutput, false,
this.outputNeurons);
result.addLayer(output);
result.getStructure().finalizeStructure();
result.reset();
return result;
}
/**
* Set the activation function to use on each of the layers.
*
* @param activation
* The activation function.
*/
public void setActivationFunction(final ActivationFunction activation) {
this.activationHidden = activation;
}
/**
* Set the number of input neurons.
*
* @param count
* Neuron count.
*/
public void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the number of output neurons.
*
* @param count
* Neuron count.
*/
public void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
/**
* @return the activationOutput
*/
public ActivationFunction getActivationOutput() {
return activationOutput;
}
/**
* @param activationOutput the activationOutput to set
*/
public void setActivationOutput(ActivationFunction activationOutput) {
this.activationOutput = activationOutput;
}
}