/*
* 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.
*
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package org.encog.examples.neural.forest.feedforward;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.buffer.BufferedNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.logic.FeedforwardLogic;
import org.encog.normalize.DataNormalization;
import org.encog.persist.EncogPersistedCollection;
import org.encog.util.simple.EncogUtility;
public class TrainNetwork {
public static BasicNetwork generateNetwork(NeuralDataSet trainingSet) {
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(new ActivationSigmoid(), true,
trainingSet.getInputSize()));
network.addLayer(new BasicLayer(new ActivationSigmoid(), true,
Constant.HIDDEN_COUNT));
network.addLayer(new BasicLayer(new ActivationSigmoid(), true,
trainingSet.getIdealSize()));
network.setLogic(new FeedforwardLogic());
network.getStructure().finalizeStructure();
network.reset();
return network;
}
public void train(boolean useGUI) {
System.out.println("Converting training file to binary");
EncogPersistedCollection encog = new EncogPersistedCollection(
Constant.TRAINED_NETWORK_FILE);
DataNormalization norm = (DataNormalization) encog
.find(Constant.NORMALIZATION_NAME);
EncogUtility.convertCSV2Binary(Constant.NORMALIZED_FILE,
Constant.BINARY_FILE, norm.getNetworkInputLayerSize(), norm
.getNetworkOutputLayerSize(), false);
BufferedNeuralDataSet trainingSet = new BufferedNeuralDataSet(
Constant.BINARY_FILE);
BasicNetwork network = (BasicNetwork) encog
.find(Constant.TRAINED_NETWORK_NAME);
if (network == null)
network = EncogUtility.simpleFeedForward(norm
.getNetworkInputLayerSize(), Constant.HIDDEN_COUNT, 0, norm
.getNetworkOutputLayerSize(), false);
if (useGUI) {
EncogUtility.trainDialog(network, trainingSet);
} else {
EncogUtility.trainConsole(network, trainingSet,
Constant.TRAINING_MINUTES);
}
System.out.println("Training complete, saving network...");
encog.add(Constant.TRAINED_NETWORK_NAME, network);
}
}