/*
* Encog(tm) Examples v2.4
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
*
* Copyright 2008-2010 by Heaton Research Inc.
*
* Released under the LGPL.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
* the License, or (at your option) any later version.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
*
* Encog and Heaton Research are Trademarks of Heaton Research, Inc.
* For information on Heaton Research trademarks, visit:
*
* http://www.heatonresearch.com/copyright.html
*/
package org.encog.examples.neural.xorscg;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.neural.data.NeuralData;
import org.encog.neural.data.NeuralDataPair;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.logic.FeedforwardLogic;
import org.encog.neural.networks.training.propagation.scg.ScaledConjugateGradient;
import org.encog.neural.networks.training.strategy.RequiredImprovementStrategy;
import org.encog.util.logging.Logging;
/**
* XOR: This example is essentially the "Hello World" of neural network
* programming. This example shows how to construct an Encog neural
* network to predict the output from the XOR operator. This example
* uses SCG to train the neural network.
*
* @author $Author$
* @version $Revision$
*/
public class XorSCG {
public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
{ 0.0, 1.0 }, { 1.0, 1.0 } };
public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
public static void main(final String args[]) {
Logging.stopConsoleLogging();
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,1));
network.setLogic(new FeedforwardLogic());
network.getStructure().finalizeStructure();
network.reset();
(new RangeRandomizer(-5,5)).randomize(network);
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
// train the neural network
final ScaledConjugateGradient train = new ScaledConjugateGradient(network, trainingSet);
// reset if improve is less than 1% over 5 cycles
train.addStrategy(new RequiredImprovementStrategy(5));
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while(train.getError() > 0.01 );
// test the neural network
System.out.println("Neural Network Results:");
for(NeuralDataPair pair: trainingSet ) {
final NeuralData output = network.compute(pair.getInput());
System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
}
}
}