XORFlat.java example

Explorer
VAFusion2-master
/*
 * 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.
 * 
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 * For information on Heaton Research trademarks, visit:
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 * http://www.heatonresearch.com/copyright.html
 */

package org.encog.examples.neural.xorflat;

import org.encog.Encog;
import org.encog.engine.network.flat.FlatNetwork;
import org.encog.engine.network.train.prop.TrainFlatNetworkResilient;
import org.encog.neural.data.NeuralDataPair;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
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
 * a flat neural network.
 */
public class XORFlat {

	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();
		
		FlatNetwork network = new FlatNetwork(2,4,0,1,false);
		network.randomize();
		
		NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
		
		
		TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(network,trainingSet);
		
		//Encog.getInstance().initCL();
		//train.setTargetDevice(Encog.getInstance().getCL().getDevices().get(0));
		
		int epoch = 1;

		do {
			train.iteration();
			System.out
					.println("Epoch #" + epoch + " Error:" + train.getError());
			epoch++;
		} while(train.getError() > 0.01 );

		double[] output = new double[1];
		// test the neural network
		System.out.println("Neural Network Results:");
		for(NeuralDataPair pair: trainingSet ) {
			double[] input = pair.getInput().getData();
			network.compute(input, output);
			System.out.println(input[0] + "," + input[1] + ":" + output[0]);
		}
	}
}