Xor.java

package com.test;

import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.strategy.StopTrainingStrategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.Propagation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.persist.EncogDirectoryPersistence;
import org.encog.util.simple.EncogUtility;

import java.io.File;

public class Xor {
    private static final String FILENAME = "network.eg";
    private static final double[][] XOR_INPUT = {{0.0, 0.0}, {0.0, 1.0}, {1.0, 0.0}, {1.0, 1.0}};
    private static final double[][] XOR_IDEAL = {{0.0}, {1.0}, {1.0}, {0.0}};

    public static void main(String[] args) {
        trainNetwork();
        loadAndEvaluate();
    }

    private static void trainNetwork() {
        // Create a simple 2-3-1 feedforward network with tanh activation functions
        BasicNetwork network = EncogUtility.simpleFeedForward(2, 3, 0, 1, true);

        MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
        Propagation train = new Backpropagation(network, trainingSet);

        int epoch = 1;
        do {
            train.iteration();
            System.out.println("Epoch #" + epoch + " Error: " + train.getError());
            epoch++;
        } while (train.getError() > 0.009);

        double e = network.calculateError(trainingSet);
        System.out.println("Network trained to error: " + e);
        System.out.println("Saving network...");

        EncogDirectoryPersistence.saveObject(new File(FILENAME), network);
    }

    private static void loadAndEvaluate() {
        System.out.println("Loading Network...");
        BasicNetwork network = (BasicNetwork) EncogDirectoryPersistence.loadObject(new File(FILENAME));
        BasicMLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

        double e = network.calculateError(trainingSet);
        System.out.println("Loaded network's error is (should be the same as above):" + e);
    }
}