XORConst.java

/*
 * Encog(tm) Java Examples v3.4
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-examples
 *
 * 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.examples.neural.xor;
 
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
 
public class XORConst {
 
	/**
	 * The input necessary for XOR.
	 */
	public static double XOR_INPUT[][] = { { 1.0, 0.0 }, { 0.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };
 
	/**
	 * The ideal data necessary for XOR.
	 */
	public static double XOR_IDEAL[][] = { { 1.0 }, { 0.0 }, { 1.0 }, { 0.0 } };
 
	/**
	 * The main method.
	 * @param args No arguments are used.
	 */
	public static void main(final String args[]) {
 
		// create a neural network, without using a factory
		BasicNetwork network = new BasicNetwork();
		network.addLayer(new BasicLayer(null,true,2));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
		network.getStructure().finalizeStructure();
		network.reset();
		new ConsistentRandomizer(-1,1,500).randomize(network);
		System.out.println(network.dumpWeights());
 
		// create training data
		MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
 
		// train the neural network
		final Backpropagation train = new Backpropagation(network, trainingSet, 0.7, 0.3);
		train.fixFlatSpot(false);
 
		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(MLDataPair pair: trainingSet ) {
			final MLData 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));
		}
 
		Encog.getInstance().shutdown();
	}
}