/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 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.freeform.training;
import java.io.Serializable;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.freeform.FreeformConnection;
import org.encog.neural.freeform.FreeformNetwork;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
/**
* Perform backpropagation for a freeform neural network.
*/
public class FreeformBackPropagation extends FreeformPropagationTraining
implements Serializable {
/**
* The serial ID.
*/
private static final long serialVersionUID = 1L;
/**
* The learning rate. The coefficient for how much of the gradient is applied to each weight.
*/
private final double learningRate;
/**
* The momentum. The coefficient for how much of the previous delta is applied to each weight.
* In theory, prevents local minima stall.
*/
private final double momentum;
/**
* Construct a back propagation trainer.
* @param theNetwork The network to train.
* @param theTraining The training data to use. The coefficient for how much of the gradient is applied to each weight.
* @param theLearningRate The learning rate. The coefficient for how much of the previous delta is applied to each weight.
* In theory, prevents local minima stall.
* @param theMomentum The momentum.
*/
public FreeformBackPropagation(final FreeformNetwork theNetwork,
final MLDataSet theTraining, final double theLearningRate,
final double theMomentum) {
super(theNetwork, theTraining);
theNetwork.tempTrainingAllocate(1, 2);
this.learningRate = theLearningRate;
this.momentum = theMomentum;
}
/**
* {@inheritDoc}
*/
@Override
protected void learnConnection(final FreeformConnection connection) {
final double gradient = connection.getTempTraining(0);
final double delta = (gradient * this.learningRate)
+ (connection.getTempTraining(1) * this.momentum);
connection.setTempTraining(1, delta);
connection.addWeight(delta);
}
/**
* {@inheritDoc}
*/
@Override
public TrainingContinuation pause() {
// TODO Auto-generated method stub
return null;
}
/**
* {@inheritDoc}
*/
@Override
public void resume(final TrainingContinuation state) {
// TODO Auto-generated method stub
}
}