package org.encog.examples.neural.benchmark;
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.neural.flat.train.prop.RPROPType;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
public class TestConverge {
/**
* 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[]) {
int failureCount = 0;
for(int i=0;i<1000;i++) {
// 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(0,0.5,i)).randomize(network);
// create training data
MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
// train the neural network
final ResilientPropagation train = new ResilientPropagation(network, trainingSet);
train.setRPROPType(RPROPType.iRPROPp);
int epoch = 1;
do {
train.iteration();
epoch++;
} while (train.getError() > 0.01 && epoch<1000 );
if( epoch>900 ) {
failureCount++;
}
}
System.out.println("Failed: " + failureCount);
Encog.getInstance().shutdown();
}
}