Redes Neurais: Exemplo Encog

/*
 * Encog(tm) Examples v3.1 - Java Version
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 
 * Copyright 2008-2012 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
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package org.encog.examples.neural.xor;

import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
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.resilient.ResilientPropagation;

/**
 * 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 backpropagation to train the neural network.
 * 
 * This example attempts to use a minimum of Encog features to create and
 * train the neural network.  This allows you to see exactly what is going
 * on.  For a more advanced example, that uses Encog factories, refer to
 * the XORFactory example.
 * 
 */
public class XORHelloWorld {

	/**
	 * The input necessary for XOR.
	 */
	public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };

	/**
	 * The ideal data necessary for XOR.
	 */
	public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 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,3));
		network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
		network.getStructure().finalizeStructure();
		network.reset();

		// create training data
		MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
		
		// train the neural network
		final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

		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();
	}
}