/*
* 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:
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*/
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.resilient.RPROPConst;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
/**
* A training definition for RPROP training.
*/
public class RPROPJob extends TrainingJob {
/**
* The initial update value.
*/
private double initialUpdate = RPROPConst.DEFAULT_INITIAL_UPDATE;
/**
* The maximum step value.
*/
private double maxStep = RPROPConst.DEFAULT_MAX_STEP;
/**
* Construct an RPROP job. For more information on RPROP see the
* ResilientPropagation class.
*
* @param network
* The network to train.
* @param training
* The training data to use.
* @param loadToMemory
* True if binary training data should be loaded to memory.
*/
public RPROPJob(final BasicNetwork network, final MLDataSet training,
final boolean loadToMemory) {
super(network, training, loadToMemory);
}
/**
* {@inheritDoc}
*/
@Override
public void createTrainer(final boolean singleThreaded) {
final Propagation train = new ResilientPropagation(getNetwork(),
getTraining(), getInitialUpdate(), getMaxStep());
if (singleThreaded) {
train.setThreadCount(1);
} else {
train.setThreadCount(0);
}
for (final Strategy strategy : getStrategies()) {
train.addStrategy(strategy);
}
setTrain(train);
}
/**
* @return the initialUpdate
*/
public double getInitialUpdate() {
return this.initialUpdate;
}
/**
* @return the maxStep
*/
public double getMaxStep() {
return this.maxStep;
}
/**
* @param initialUpdate
* the initialUpdate to set
*/
public void setInitialUpdate(final double initialUpdate) {
this.initialUpdate = initialUpdate;
}
/**
* @param maxStep
* the maxStep to set
*/
public void setMaxStep(final double maxStep) {
this.maxStep = maxStep;
}
}