/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.examples.neural.recurrent.elman;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.examples.neural.util.TemporalXOR;
import org.encog.mathutil.error.ErrorCalculation;
import org.encog.mathutil.error.ErrorCalculationMode;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.ml.train.strategy.Greedy;
import org.encog.ml.train.strategy.HybridStrategy;
import org.encog.ml.train.strategy.StopTrainingStrategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.CalculateScore;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing;
import org.encog.neural.networks.training.propagation.Propagation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.neural.pattern.ElmanPattern;
import org.encog.neural.pattern.FeedForwardPattern;
/**
* Implement an Elman style neural network with Encog. This network attempts to
* predict the next value in an XOR sequence, taken one at a time. A regular
* feedforward network would fail using a single input neuron for this task. The
* internal state stored by an Elman neural network allows better performance.
* Elman networks are typically used for temporal neural networks. An Elman
* network has a single context layer connected to the hidden layer.
*
* @author jeff
*
*/
public class ElmanXOR {
static BasicNetwork createElmanNetwork() {
// construct an Elman type network
ElmanPattern pattern = new ElmanPattern();
pattern.setActivationFunction(new ActivationSigmoid());
pattern.setInputNeurons(1);
pattern.addHiddenLayer(6);
pattern.setOutputNeurons(1);
return (BasicNetwork)pattern.generate();
}
static BasicNetwork createFeedforwardNetwork() {
// construct a feedforward type network
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.setActivationFunction(new ActivationSigmoid());
pattern.setInputNeurons(1);
pattern.addHiddenLayer(6);
pattern.setOutputNeurons(1);
return (BasicNetwork)pattern.generate();
}
public static void main(final String args[]) {
final TemporalXOR temp = new TemporalXOR();
final MLDataSet trainingSet = temp.generate(120);
final BasicNetwork elmanNetwork = ElmanXOR.createElmanNetwork();
final BasicNetwork feedforwardNetwork = ElmanXOR
.createFeedforwardNetwork();
final double elmanError = ElmanXOR.trainNetwork("Elman", elmanNetwork,
trainingSet);
final double feedforwardError = ElmanXOR.trainNetwork("Feedforward",
feedforwardNetwork, trainingSet);
System.out.println("Best error rate with Elman Network: " + elmanError);
System.out.println("Best error rate with Feedforward Network: "
+ feedforwardError);
System.out
.println("Elman should be able to get into the 30% range,\nfeedforward should not go below 50%.\nThe recurrent Elment net can learn better in this case.");
System.out
.println("If your results are not as good, try rerunning, or perhaps training longer.");
}
public static double trainNetwork(final String what,
final BasicNetwork network, final MLDataSet trainingSet) {
// train the neural network
CalculateScore score = new TrainingSetScore(trainingSet);
final MLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);
((Propagation)trainMain).setNumThreads(1);
final StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.addStrategy(new Greedy());
trainMain.addStrategy(new HybridStrategy(trainAlt));
trainMain.addStrategy(stop);
int epoch = 0;
while (!stop.shouldStop()) {
trainMain.iteration();
System.out.println("Training " + what + ", Epoch #" + epoch
+ " Error:" + trainMain.getError());
epoch++;
}
return trainMain.getError();
}
}