/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.nnet.learning;
import java.util.List;
import org.neuroph.core.Connection;
import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.transfer.TransferFunction;
/**
* Back Propagation learning rule for Multi Layer Perceptron neural networks.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*
*/
public class BackPropagation extends SigmoidDeltaRule {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Creates new instance of BackPropagation learning
*/
public BackPropagation() {
super();
}
/**
* This method implements weight update procedure for the whole network
* for the specified error vector
*
* @param patternError
* single pattern error vector
*/
@Override
protected void updateNetworkWeights(double[] patternError) {
this.adjustOutputNeurons(patternError);
this.adjustHiddenLayers();
}
/**
* This method implements weights adjustment for the hidden layers
*/
protected void adjustHiddenLayers() {
int layerNum = this.neuralNetwork.getLayersCount();
for (int i = layerNum - 2; i > 0; i--) {
Layer layer = neuralNetwork.getLayerAt(i);
for(Neuron neuron : layer.getNeurons()) {
double delta = this.calculateDelta(neuron);
neuron.setError(delta);
this.updateNeuronWeights(neuron);
} // for
} // for
}
/**
* Calculates and returns delta parameter (neuron error) for the specified
* neuron
*
* @param neuron
* neuron to calculate error for
* @return delta (neuron error) for the specified neuron
*/
private double calculateDelta(Neuron neuron) {
List<Connection> connectedTo = ((Neuron) neuron).getOutConnections();
double delta_sum = 0d;
for(Connection connection : connectedTo) {
double d = connection.getToNeuron().getError()
* connection.getWeight().getValue();
delta_sum += d; // weighted sum from the next layer
} // for
TransferFunction transferFunction = neuron.getTransferFunction();
double netInput = neuron.getNetInput();
double f1 = transferFunction.getDerivative(netInput);
double delta = f1 * delta_sum;
return delta;
}
}