/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.plugins.opencl.example;
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.plugins.opencl.EncogOpenCLPlugin;
import org.encog.util.simple.EncogUtility;
/**
* 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 OpenCLXor {
/**
* 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[]) {
Encog.getInstance().registerPlugin(new EncogOpenCLPlugin());
// create a neural network, without using a factory
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,false,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,1));
network.getStructure().finalizeStructure();
network.reset();
new ConsistentRandomizer(-1,1).randomize(network);
// create training data
MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
final MLTrain 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 && epoch<5000);
EncogUtility.evaluate(network, trainingSet);
}
}