xortrainer.java

这是一个神经网络的开发工具

/*
 * XORTrainer.java
 *
 * Created on 3 marzo 2004, 17.26
 */

package org.joone.samples.engine.xor;

import org.joone.engine.*;
import org.joone.net.*;
import org.joone.io.*;

import java.io.*;
import java.util.*;

/**
 * This sample application loads a serialized network and launches it in training mode
 * @author  P.Marrone
 */
public class XORTrainer implements NeuralNetListener {
    
    /** Creates a new instance of XORTrainer */
    public XORTrainer() {
    }
    
    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
        if (args.length < 1) {
            System.out.println("Usage: java org.joone.samples.engine.xor.XORTrainer <neuralNet>");
            System.out.println("where <neuralNet> is the file name (with its complete path) that contains the serialized XOR.snet");
        }
        else {
            XORTrainer xor = new XORTrainer();
            xor.Go(args[0]);
        }
    }
    
    private void Go(String fileName) {
        NeuralNet xor = restoreNeuralNet(fileName);
        if (xor != null) {
            xor.getMonitor().addNeuralNetListener(this);
            xor.getMonitor().setLearning(true);
            TreeSet tree = xor.check();
            if (tree.isEmpty()) {
                xor.start();
                xor.getMonitor().Go();
                xor.join();
                NeuralNet cXor = xor.cloneNet();
                System.out.println("Network stopped. Last RMSE="+cXor.getMonitor().getGlobalError());
            }
            else {
                Iterator it = tree.iterator();
                while (it.hasNext()) {
                    NetCheck nc = (NetCheck)it.next();
                    System.out.println(nc.toString());
                }
            }
            
        }
    }
    
    private NeuralNet restoreNeuralNet(String fileName) {
        NeuralNet nnet = null;
        try {
            FileInputStream stream = new FileInputStream(fileName);
            ObjectInput input = new ObjectInputStream(stream);
            nnet = (NeuralNet)input.readObject();
        }
        catch (Exception e) {
            System.out.println( "Exception was thrown. Message is : " + e.getMessage());
        }
        return nnet;
    }
    
    public void cicleTerminated(NeuralNetEvent e) {
    }
    
    public void errorChanged(NeuralNetEvent e) {
        Monitor mon = (Monitor) e.getSource();
        long	c = mon.getCurrentCicle();
        long	cl = c / 500;
        
        // We want to print the results every 1000 cycles
        if ((cl * 500) == c) {
            System.out.println(c + " cycles remaining - Error = " + mon.getGlobalError());
        }
    }
    
    public void netStarted(NeuralNetEvent e) {
        System.out.println("Started...");
    }
    
    public void netStopped(NeuralNetEvent e) {
        System.out.println("Stopped...");
    }
    
    public void netStoppedError(NeuralNetEvent e, String error) {
        System.out.println("Error: "+error);
        System.exit(1);
    }
    
}