/*
* Encog(tm) Examples v2.4
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
*
* Copyright 2008-2010 by Heaton Research Inc.
*
* Released under the LGPL.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
* the License, or (at your option) any later version.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
*
* Encog and Heaton Research are Trademarks of Heaton Research, Inc.
* For information on Heaton Research trademarks, visit:
*
* http://www.heatonresearch.com/copyright.html
*/
package org.encog.examples.neural.xorpartial;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.synapse.Synapse;
import org.encog.util.logging.Logging;
import org.encog.util.simple.EncogUtility;
/**
* Partial neural networks. Encog allows you to remove any neuron connection in
* a fully connected neural network. This example creates a 2x10x10x1 neural
* network to learn the XOR. Several connections are removed prior to training.
*/
public class XORPartial {
public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
{ 0.0, 1.0 }, { 1.0, 1.0 } };
public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
public static void main(final String args[]) {
Logging.stopConsoleLogging();
BasicNetwork network = EncogUtility.simpleFeedForward(2, 10, 10, 1,
false);
network.reset();
// obtain some of the synapses that we wish to remove connections from
Layer inputLayer = network.getLayer(BasicNetwork.TAG_INPUT);
Synapse inputToHidden1 = inputLayer.getNext().get(0);
Synapse hidden1ToHidden2 = inputToHidden1.getToLayer().getNext().get(0);
Synapse hidden2ToOutput = inputToHidden1.getToLayer().getNext().get(0);
// remove the connection from input neuron 0 to hidden1 neuron 1.
network.enableConnection(inputToHidden1, 0, 1, false);
// remove the connection from hidden1 neuron 2 to hidden2 neuron 3.
network.enableConnection(hidden1ToHidden2, 2, 3, false);
// remove the connection from hidden2 neuron 3 to output neuron 4.
network.enableConnection(hidden2ToOutput, 3, 4, false);
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
EncogUtility.trainToError(network, trainingSet, 0.01);
System.out
.println("Training should leave hidden neuron weights at zero.");
System.out.println("First removed neuron weight:"
+ inputToHidden1.getMatrix().get(0, 1));
System.out.println("First removed neuron weight:"
+ hidden1ToHidden2.getMatrix().get(2, 3));
System.out.println("First removed neuron weight:"
+ hidden2ToOutput.getMatrix().get(3, 4));
System.out.println("Final output:");
EncogUtility.evaluate(network, trainingSet);
}
}