/*
* Encog(tm) Java Examples v3.4
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-examples
*
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.examples.neural.freeform;
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.freeform.FreeformLayer;
import org.encog.neural.freeform.FreeformNetwork;
import org.encog.util.simple.EncogUtility;
public class SkipNeuralNetwork {
/**
* 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
FreeformNetwork network = new FreeformNetwork();
FreeformLayer inputLayer = network.createInputLayer(2);
FreeformLayer hiddenLayer1 = network.createLayer(3);
FreeformLayer outputLayer = network.createOutputLayer(1);
network.connectLayers(inputLayer, hiddenLayer1, new ActivationSigmoid(), 1.0, false);
network.connectLayers(hiddenLayer1, outputLayer, new ActivationSigmoid(), 1.0, false);
network.connectLayers(inputLayer, outputLayer, new ActivationSigmoid(), 0.0, false);
network.reset();
// create training data
MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
//MLTrain train = new FreeformBackPropagation(network, trainingSet, 0.7, 0.3);
//EncogUtility.trainToError(train, 0.01);
EncogUtility.trainToError(network, trainingSet, 0.01);
EncogUtility.evaluate(network, trainingSet);
Encog.getInstance().shutdown();
}
}