神经网络开发包joone的功能演示

import java.io.File;

import org.joone.engine.FullSynapse;
import org.joone.engine.LinearLayer;
import org.joone.engine.Monitor;
import org.joone.engine.SigmoidLayer;
import org.joone.engine.learning.TeachingSynapse;
import org.joone.io.FileInputSynapse;
import org.joone.io.FileOutputSynapse;
import org.joone.io.InputSwitchSynapse;
import org.joone.net.NeuralNet;

public class Main {
private NeuralNetnnet = null;
private FileInputSynapse inputSynapse1, inputSynapse2, inputSynapse3;
private FileInputSynapse desiredSynapse1, desiredSynapse2, desiredSynapse3;
// private MemoryOutputSynapse outputSynapse;
private InputSwitchSynapse inputSw, desiredSw;
private static String inputFile = "src/test.txt";

/**
* @param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub

Main m = new Main();
m.test();
m.train();
}

public void train() {

// get the monitor object to train or feed forward
Monitor monitor = nnet.getMonitor();

// set the monitor parameters
monitor.setLearningRate(0.8);
monitor.setMomentum(0.3);
monitor.setTrainingPatterns(4);
monitor.setTotCicles(1000);
monitor.setLearning(true);
nnet.addNeuralNetListener(new Test());
interrogate();
interrogate();
interrogate();

}

private void interrogate() {
// neuralNet is an instance of NeuralNet
Monitor monitor=nnet.getMonitor();
monitor.setTotCicles(1);
monitor.setLearning(false);
FileOutputSynapse output=new FileOutputSynapse();
// set the output synapse to write the output of the net
output.setFileName("src/xorOut.txt");
// inject the input and get the output
if(nnet!=null) {
nnet.addOutputSynapse(output);
System.out.println(nnet.check());
nnet.start();
monitor.Go();
nnet.join();
}


}

public void test() {
LinearLayerinput = new LinearLayer();
SigmoidLayerhidden = new SigmoidLayer();
SigmoidLayeroutput = new SigmoidLayer();

input.setRows(2);
hidden.setRows(3);
output.setRows(1);

input.setLayerName("inputLayer");
hidden.setLayerName("hiddenLayer");
output.setLayerName("outputLayer");

FullSynapse synapse_IH = new FullSynapse();/* input -> hidden conn. */
FullSynapse synapse_HO = new FullSynapse();/* hidden -> output conn. */

input.addOutputSynapse(synapse_IH);
hidden.addInputSynapse(synapse_IH);

hidden.addOutputSynapse(synapse_HO);
output.addInputSynapse(synapse_HO);

inputSynapse1 = new FileInputSynapse();
inputSynapse2 = new FileInputSynapse();
inputSynapse3 = new FileInputSynapse();



inputSynapse1.setInputFile(new File(inputFile));
inputSynapse1.setName("input1");
inputSynapse1.setAdvancedColumnSelector("1-2");
inputSynapse1.setFirstRow(1);
inputSynapse1.setLastRow(4);

inputSynapse2.setInputFile(new File(inputFile));
inputSynapse2.setName("input2");
inputSynapse2.setAdvancedColumnSelector("1-2");
inputSynapse2.setFirstRow(2);
inputSynapse2.setLastRow(3);

inputSynapse3.setInputFile(new File(inputFile));
inputSynapse3.setName("input3");
inputSynapse3.setAdvancedColumnSelector("1-2");
inputSynapse3.setFirstRow(4);
inputSynapse3.setLastRow(4);

inputSw = new InputSwitchSynapse();
inputSw.addInputSynapse(inputSynapse1);
inputSw.addInputSynapse(inputSynapse2);
inputSw.addInputSynapse(inputSynapse3);

input.addInputSynapse(inputSw);

// The Trainer and its desired output
desiredSynapse1 = new FileInputSynapse();
desiredSynapse2 = new FileInputSynapse();
desiredSynapse3 = new FileInputSynapse();

desiredSynapse1.setInputFile(new File(inputFile));
desiredSynapse1.setName("desired1");
desiredSynapse1.setAdvancedColumnSelector("3");
desiredSynapse1.setFirstRow(1);
desiredSynapse1.setLastRow(4);

desiredSynapse2.setInputFile(new File(inputFile));
desiredSynapse2.setName("desired2");
desiredSynapse2.setAdvancedColumnSelector("3");
desiredSynapse2.setFirstRow(2);
desiredSynapse2.setLastRow(3);

desiredSynapse3.setInputFile(new File(inputFile));
desiredSynapse3.setName("desired3");
desiredSynapse3.setAdvancedColumnSelector("3");
desiredSynapse3.setFirstRow(4);
desiredSynapse3.setLastRow(4);

desiredSw = new InputSwitchSynapse();
desiredSw.addInputSynapse(desiredSynapse1);
desiredSw.addInputSynapse(desiredSynapse2);
desiredSw.addInputSynapse(desiredSynapse3);

TeachingSynapse trainer = new TeachingSynapse();
trainer.setDesired(desiredSw);

// Now we add this structure to a NeuralNet object
nnet = new NeuralNet();

nnet.addLayer(input, NeuralNet.INPUT_LAYER);
nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER);
nnet.addLayer(output, NeuralNet.OUTPUT_LAYER);
nnet.setTeacher(trainer);
output.addOutputSynapse(trainer);
}

}

import org.joone.engine.Monitor;
import org.joone.engine.NeuralNetEvent;
import org.joone.engine.NeuralNetListener;

public class Test implements NeuralNetListener {

@Override
public void cicleTerminated(NeuralNetEvent arg0) {
// TODO Auto-generated method stub

}

@Override
public void errorChanged(NeuralNetEvent e) {
// TODO Auto-generated method stub
Monitor mon = (Monitor)e.getSource();
System.out.println("Cycle: "+(mon.getTotCicles()-mon.getCurrentCicle())+" RMSE:"+mon.getGlobalError());

}

@Override
public void netStarted(NeuralNetEvent arg0) {
// TODO Auto-generated method stub
System.out.println("Training...");
}

@Override
public void netStopped(NeuralNetEvent arg0) {
// TODO Auto-generated method stub
System.out.println("Stopped");
}

@Override
public void netStoppedError(NeuralNetEvent arg0, String arg1) {
// TODO Auto-generated method stub

}

}