an Object Oriented Neural Engine / Discussion / Help: Problem: XOR Nested ANN only through java

Hello everyone,
I go straight to the point
I'm trying to study neural networks with java, I have some questions about JOONE I hope you can help me
1) How do I IMPORT and USE through java code a .snet neural network, I mean: Once imported, how can I use it in my java code? I successfully exported the network created (XOR), but I can not properly load and use the trained network, I do not know where I make the mistake
the model considered is the XOR one.

a)ExtractTrainedNet (performs the extraction of a previously saved neural network, the network works fine, it has been tested with the editor)

package xor;
import java.io.FileOutputStream;
import java.io.ObjectOutputStream;
import org.joone.engine.Layer;
import org.joone.net.NeuralNet;
import org.joone.net.NeuralNetLoader;
public class ExtractTrainedNet {

    public ExtractTrainedNet(){

    }
    /**
     * @param args
     */
    public static void main(String[] args) {
        ExtractTrainedNet ex = new ExtractTrainedNet();
        ex.extract();

    }

    public void extract (){
        restoreNeuralNet("C:\\Users\\...\\Desktop\\saveNetXOR.snet");
        NeuralNet xorNNet = this.restoreNeuralNet("C:\\Users\\...\\Desktop\\saveNetXOR.snet");
        if (xorNNet != null) {
            Layer inputI = xorNNet.getInputLayer();
            inputI.removeAllInputs();

            Layer outputO = xorNNet.getOutputLayer();
            outputO.removeAllOutputs();

            String fileName= "C:\\Users\\...\\Desktop\\TraindeXOR.snet";
            try {

                FileOutputStream stream = new FileOutputStream(fileName);
                ObjectOutputStream out = new ObjectOutputStream(stream);
                out.writeObject(xorNNet);
                out.close();
                } catch (Exception excp) {
                excp.printStackTrace();
                }
        }
    }

    public NeuralNet restoreNeuralNet(String fileName) {
        NeuralNetLoader loader = new NeuralNetLoader(fileName);
        NeuralNet nnet = loader.getNeuralNet();
        return nnet;
        }
}

b)NestedANN
(This code will take care of the trained network previously saved, INPUT data are read from a text file, the result is written to another file) the network created is:

INPUT Layer (.txt) -> NestedANN (trained neurla network) -> OUTPUT Layer (.txt)

the goal is to create all alone through JAVA code

package xor;
import java.io.File;
import java.io.FileOutputStream;
import java.io.ObjectOutputStream;
import org.joone.engine.*;
import org.joone.engine.learning.*;
import org.joone.io.*;
import org.joone.net.NestedNeuralLayer;
import org.joone.net.NeuralNet;
import org.joone.net.NeuralNetLoader;
public class NestedANN implements NeuralNetListener {

    public NestedANN(){

    }
    /**
     * @param args
     */
    public static void main(String[] args) {
        NestedANN nsta = new NestedANN();
        nsta.start_nestedNet();
    }

    public void start_nestedNet (){

        String trainedNeuralNet = "C:\\Users\\...\\Desktop\\TraindeXOR.snet";
        /*
         * Create the three layers
         */

        // Input Layer --> Nested ANN Layer --> OutputLayer
        LinearLayer input = new LinearLayer(); // INPUT Layer
        NestedNeuralLayer nest = new NestedNeuralLayer(); // NestedNerual Layer
        //SigmoidLayer output = new SigmoidLayer(); // Output Layer
        LinearLayer output = new LinearLayer(); // OUTPUT Layer

        /*
         * Set layer dimension (INPUT, NestedNeural, OUTPUT)
         */
        input.setRows(200); // INPUT
        nest.setNeuralNet(trainedNeuralNet); // NestedNeural
        output.setRows(1); // OUTPUT

        /*
         * Build the neural net connection
         */
        SangerSynapse synapse_IN = new SangerSynapse(); /* Input -> Nested connection */
        SangerSynapse synapse_NO = new SangerSynapse(); /* Nested -> Output connection */

        //Connect the INPUT with the Nested layer
        input.addOutputSynapse(synapse_IN);
        nest.addInputSynapse(synapse_IN);

        //Connect the Nested layer with the OUTPUT layer
        nest.addOutputSynapse(synapse_NO);
        output.addInputSynapse(synapse_NO);

        /*
         * Define an INPUT for the net
         */
        FileInputSynapse inputStream = new FileInputSynapse();
        /* The first two columns contain the input values */
        //inputStream.setAdvancedColumnSelector("1,2");
        inputStream.setFirstCol(1); // start Col 1
        inputStream.setLastCol(2); //  last Col 2
        /* This is the file that contains the input data */
        inputStream.setInputFile(new File("C:\\Users\\....\\Desktop\\testSet.txt"));

        //add the input synapse to the first layer, The input synapse extends the
        //Synapse object, so it can be attached to a layer like a synapse
        input.addInputSynapse(inputStream);

        //create an output synapse
        FileOutputSynapse fileOutput = new FileOutputSynapse();
        fileOutput.setFileName("C:\\Users\\...\\Desktop\\OutTestSet.txt");
        // attach the output synapse to the last layer of the NN
        output.addOutputSynapse(fileOutput);

         NeuralNet nnet = new NeuralNet();
         nnet.addLayer(input, NeuralNet.INPUT_LAYER);
         nnet.addLayer(nest);
         nnet.addLayer(output, NeuralNet.OUTPUT_LAYER);
         
        /*  
         * Set parameters for the monitor
         */
        Monitor monitor = nnet.getMonitor();
        monitor.setLearningRate(0.7); // era 0.8
        monitor.setMomentum(0.9);  //era 0.3

        /*
         * The application registers itself as a monitor's listener, so it can receive the
            notifications of termination from the net.
         */
        monitor.addNeuralNetListener(this);

        //Set all the training parameters of the net
        monitor.setTrainingPatterns(4); /* # of rows in the input file */
        monitor.setTotCicles(1); /* How many times the net must be trained*/
        nest.setLearning(false);//La rete non deve essere addestrata
        nnet.getMonitor().setLearning(false);

        //Start the nested neural network
        //nest.start();
        //monitor.setLearning(false); /* The net must be trained */
        nnet.go(); /* The network starts the training phase */      
        //Start the nested neural network
        nest.start();
    }

    //Method for restore neuralNet
    public NeuralNet restoreNeuralNet(String fileName) {
        NeuralNetLoader loader = new NeuralNetLoader(fileName);
        NeuralNet nnet = loader.getNeuralNet();
        return nnet;
        }
    public void netStopped(NeuralNetEvent e) {
        System.out.println("Training finished");
    }
    
 public void cicleTerminated(NeuralNetEvent e) {
     Monitor mon = (Monitor)e.getSource();
     long c = mon.getCurrentCicle();
     /* We want print the results every 100 epochs */
     if (c % 100 == 0){
     System.out.println(c + " epochs remaining - RMSE = " +
     mon.getGlobalError());}

     }

    
    public void netStarted(NeuralNetEvent e) {
        System.out.println("Training...");
    }
    
    public void errorChanged(NeuralNetEvent e) {
        Monitor mon = (Monitor)e.getSource();
        /* We want print the results every 200 cycles */
        if (mon.getCurrentCicle() % 200 == 0)
            System.out.println(mon.getCurrentCicle() + " epochs remaining - RMSE = " + mon.getGlobalError());
    }
    
    public void netStoppedError(NeuralNetEvent e,String error) {
    }
}

I hope I have clearly explained the problem I'm facing,
thanks for your help
Marco

Problem: XOR Nested ANN only through java

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Problem: XOR Nested ANN only through java