/*
* 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.xorunbiased;
import org.encog.engine.network.activation.ActivationSigmoid;
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.training.Train;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.util.logging.Logging;
/**
* Neural networks in Encog can be created without bias values (weight
* matrix only). Encog can still learn the XOR, even without biases. Usually
* you will want bias values, however some network types require
* unbiased layers.
*
* @author jeff
*
*/
public class XorUnBiased {
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();
final BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 2));
network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 3));
network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.getStructure().finalizeStructure();
network.reset();
final NeuralDataSet trainingSet = new BasicNeuralDataSet(
XorUnBiased.XOR_INPUT, XorUnBiased.XOR_IDEAL);
// train the neural network
final Train train = new Backpropagation(network, trainingSet, 0.1, 0.0);
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while ((epoch < 500000) && (train.getError() > 0.001));
// test the neural network
System.out.println("Neural Network Results:");
for (final 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));
}
}
}