/*
* 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.networks.training.concurrent.jobs;
import java.util.ArrayList;
import java.util.List;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.ml.train.strategy.Strategy;
import org.encog.ml.train.strategy.end.EndTrainingStrategy;
import org.encog.neural.networks.BasicNetwork;
/**
* Base class for all concurrent training jobs.
*/
public abstract class TrainingJob {
/**
* The network to train.
*/
private BasicNetwork network;
/**
* The training data to use.
*/
private MLDataSet training;
/**
* The strategies to use.
*/
private final List<Strategy> strategies = new ArrayList<Strategy>();
/**
* True, if binary training data should be loaded to memory.
*/
private boolean loadToMemory;
/**
* The trainer being used.
*/
private MLTrain train;
/**
* Holds any errors that occur during training.
*/
private Throwable error;
/**
* Construct a training job.
*
* @param network
* The network to train.
* @param training
* The training data to use.
* @param loadToMemory
* True, if binary data should be loaded to memory.
*/
public TrainingJob(final BasicNetwork network, final MLDataSet training,
final boolean loadToMemory) {
super();
this.network = network;
this.training = training;
this.loadToMemory = loadToMemory;
}
/**
* Create a trainer to use.
* @param singleThreaded Whether training is single threaded
*/
public abstract void createTrainer(boolean singleThreaded);
/**
* @return the error
*/
public Throwable getError() {
return this.error;
}
/**
* @return the network
*/
public BasicNetwork getNetwork() {
return this.network;
}
/**
* @return the strategies
*/
public List<Strategy> getStrategies() {
return this.strategies;
}
/**
* @return the train
*/
public MLTrain getTrain() {
return this.train;
}
/**
* @return the training
*/
public MLDataSet getTraining() {
return this.training;
}
/**
* @return the loadToMemory
*/
public boolean isLoadToMemory() {
return this.loadToMemory;
}
/**
* @param error
* the error to set
*/
public void setError(final Throwable error) {
this.error = error;
}
/**
* @param loadToMemory
* the loadToMemory to set
*/
public void setLoadToMemory(final boolean loadToMemory) {
this.loadToMemory = loadToMemory;
}
/**
* @param network
* the network to set
*/
public void setNetwork(final BasicNetwork network) {
this.network = network;
}
/**
* @param train
* the train to set
*/
public void setTrain(final MLTrain train) {
this.train = train;
}
/**
* @param training
* the training to set
*/
public void setTraining(final MLDataSet training) {
this.training = training;
}
/**
* @return True, if training should continue.
*/
public boolean shouldContinue() {
for (final Strategy strategy : this.train.getStrategies()) {
if (strategy instanceof EndTrainingStrategy) {
final EndTrainingStrategy end = (EndTrainingStrategy) strategy;
if (end.shouldStop()) {
return false;
}
}
}
return true;
}
}