/*
* Encog(tm) Core Unit Tests 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.neural.networks.training;
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.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
/**
* Test class to evaluate NguyenWidrowRandomizer against RangeRandomizer
*
*
* @author Stephan Corriveau
*
*/
public class EvaluateNuguyenWidrow {
public static void main( String[] args ) {
MLDataSet trainingData1 = new BasicMLDataSet( XOR.XOR_INPUT, XOR.XOR_IDEAL );
MLDataSet trainingData2 = new BasicMLDataSet( XOR.XOR_INPUT, XOR.XOR_IDEAL );
MLDataSet trainingData3 = new BasicMLDataSet( XOR.XOR_INPUT, XOR.XOR_IDEAL );
for ( int i = 0; i < 1; i++ ) {
BasicNetwork network3 = NetworkUtil.createXORNetworknNguyenWidrowUntrained();
MLTrain bpropNguyen = new Backpropagation( network3, trainingData3, 0.9, 0.8 );
train(i, bpropNguyen, "NguyenWidrowRandomizer" );
BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
MLTrain bpropRange = new Backpropagation( network2, trainingData2, 0.9, 0.8 );
train(i, bpropRange, "RangeRandomizer ");
}
}
private final static void train( long it, MLTrain train, String randomizerUsed ){
train.iteration();
double error1 = train.getError();
int epoch = 1;
do {
train.iteration();
epoch++;
} while ((epoch < 5000) && (train.getError() > 0.009 ));
double error2 = train.getError();
double improve = (error1-error2)/error1;
System.out.println( randomizerUsed + "\t" + it + "\t" + train.getError() + "\t" + epoch + "\t" + improve);
}
}