/*
* 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.networks.training.concurrent.jobs;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.strategy.Strategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.propagation.Propagation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
/**
* A training definition for BPROP training.
*/
public class BPROPJob extends TrainingJob {
/**
* The learning rate to use.
*/
private double learningRate;
/**
* The momentum to use.
*/
private double momentum;
/**
* Construct a job definition for RPROP. For more information on backprop,
* see the Backpropagation class. Use OpenCLratio of 1.0 and process one
* iteration per cycle.
*
* @param network
* The network to use.
* @param training
* The training data to use.
* @param loadToMemory
* Should binary data be loaded to memory?
* @param learningRate
* THe learning rate to use.
* @param momentum
* The momentum to use.
*/
public BPROPJob(final BasicNetwork network, final MLDataSet training,
final boolean loadToMemory, final double learningRate,
final double momentum) {
super(network, training, loadToMemory);
this.learningRate = learningRate;
this.momentum = momentum;
}
/**
* {@inheritDoc}
*/
@Override
public void createTrainer(final boolean singleThreaded) {
final Propagation train = new Backpropagation(getNetwork(),
getTraining(), getLearningRate(), getMomentum());
if (singleThreaded) {
train.setThreadCount(1);
} else {
train.setThreadCount(0);
}
for (final Strategy strategy : getStrategies()) {
train.addStrategy(strategy);
}
setTrain(train);
}
/**
* @return the learningRate
*/
public double getLearningRate() {
return this.learningRate;
}
/**
* @return the momentum
*/
public double getMomentum() {
return this.momentum;
}
/**
* @param learningRate
* the learningRate to set
*/
public void setLearningRate(final double learningRate) {
this.learningRate = learningRate;
}
/**
* @param momentum
* the momentum to set
*/
public void setMomentum(final double momentum) {
this.momentum = momentum;
}
}