/**
* 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 org.neuroph.core.learning.TrainingData;
import org.neuroph.core.Connection;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.Weight;
/**
* Backpropagation learning rule with momentum.
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class MomentumBackpropagation extends BackPropagation {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Momentum factor
*/
protected double momentum = 0.25d;
/**
* Creates new instance of MomentumBackpropagation learning
*/
public MomentumBackpropagation() {
super();
this.setTrainingDataBufferSize(2); // batch weights sum and previous weight value
}
/**
* This method implements weights update procedure for the single neuron
* for the back propagation with momentum factor
* @param neuron
* neuron to update weights
*/
@Override
protected void updateNeuronWeights(Neuron neuron) {
for(Connection connection : neuron.getInputConnections() ) {
double input = connection.getInput();
if (input == 0) {
continue;
}
// get the error for specified neuron,
double neuronError = neuron.getError();
// tanh can be used to minimise the impact of big error values, which can cause network instability
// suggested at https://sourceforge.net/tracker/?func=detail&atid=1107579&aid=3130561&group_id=238532
// double neuronError = Math.tanh(neuron.getError());
Weight weight = connection.getWeight();
double currentWeighValue = weight.getValue();
double previousWeightValue = weight.getTrainingData().get(TrainingData.PREVIOUS_WEIGHT);
double deltaWeight = this.learningRate * neuronError * input +
momentum * (currentWeighValue - previousWeightValue);
// save previous weight value
weight.getTrainingData().set(TrainingData.PREVIOUS_WEIGHT, currentWeighValue);
this.applyWeightChange(weight, deltaWeight);
}
}
/**
* Returns the momentum factor
*
* @return momentum factor
*/
public double getMomentum() {
return momentum;
}
/**
* Sets the momentum factor
*
* @param momentum
* momentum factor
*/
public void setMomentum(double momentum) {
this.momentum = momentum;
}
}