/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.samples;
import java.util.Arrays;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.learning.SupervisedTrainingElement;
import org.neuroph.core.learning.TrainingElement;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.nnet.Neuroph;
import org.neuroph.nnet.learning.MomentumBackpropagation;
import org.neuroph.util.TransferFunctionType;
/**
* This sample shows how to create, train, save and load simple Multi Layer Perceptron
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class XorMultiLayerPerceptronSample {
/**
* Runs this sample
*/
public static void main(String[] args) {
// create training set (logical XOR function)
TrainingSet trainingSet = new TrainingSet(2, 1);
trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{1}));
trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{1}));
trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{0}));
// create multi layer perceptron
MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
// enable batch if using MomentumBackpropagation
if( myMlPerceptron.getLearningRule() instanceof MomentumBackpropagation )
((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setBatchMode(true);
// learn the training set
System.out.println("Training neural network...");
myMlPerceptron.learnInSameThread(trainingSet);
// test perceptron
System.out.println("Testing trained neural network");
testNeuralNetwork(myMlPerceptron, trainingSet);
// save trained neural network
myMlPerceptron.save("myMlPerceptron.nnet");
// load saved neural network
NeuralNetwork loadedMlPerceptron = NeuralNetwork.load("myMlPerceptron.nnet");
// test loaded neural network
System.out.println("Testing loaded neural network");
testNeuralNetwork(loadedMlPerceptron, trainingSet);
}
/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param trainingSet training set
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, TrainingSet trainingSet) {
for(TrainingElement trainingElement : trainingSet.trainingElements()) {
neuralNet.setInput(trainingElement.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString( trainingElement.getInput() ) );
System.out.println(" Output: " + Arrays.toString( networkOutput) );
}
}
}