{% codeblock lang:java %} import org.encog.Encog; import org.encog.engine.network.activation.ActivationReLU; import org.encog.engine.network.activation.ActivationSigmoid; import org.encog.ml.data.MLData; import org.encog.ml.data.MLDataPair; import org.encog.ml.data.MLDataSet; import org.encog.ml.data.basic.BasicMLDataSet; import org.encog.neural.networks.BasicNetwork; import org.encog.neural.networks.layers.BasicLayer; import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
public class XORHelloWorld {
/** * The input necessary for XOR. */ public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 1.0 } }; /** * The ideal data necessary for XOR. */ public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } }; /** * The main method. * @param args No arguments are used. */ public static void main(final String args[]) { // create a neural network, without using a factory BasicNetwork network = new BasicNetwork(); network.addLayer(new BasicLayer(null,true,2)); network.addLayer(new BasicLayer(new ActivationReLU(),true,5)); network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1)); network.getStructure().finalizeStructure(); network.reset(); // create training data MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL); // train the neural network final ResilientPropagation train = new ResilientPropagation(network, trainingSet); int epoch = 1; do { train.iteration(); System.out.println("Epoch #" + epoch + " Error:" + train.getError()); epoch++; } while(train.getError() > 0.01); train.finishTraining(); // test the neural network System.out.println("Neural Network Results:"); for(MLDataPair pair: trainingSet ) { final MLData output = network.compute(pair.getInput()); System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1) + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0)); } Encog.getInstance().shutdown(); } } {% endcodeblock}