/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.
*
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* and trademarks visit:
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*/
package org.encog.examples.neural.benchmark;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.benchmark.RandomTrainingFactory;
public class MultiBench {
public static final int INPUT_COUNT = 40;
public static final int HIDDEN_COUNT = 60;
public static final int OUTPUT_COUNT = 20;
public static BasicNetwork generateNetwork()
{
final BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(MultiBench.INPUT_COUNT));
network.addLayer(new BasicLayer(MultiBench.HIDDEN_COUNT));
network.addLayer(new BasicLayer(MultiBench.OUTPUT_COUNT));
network.getStructure().finalizeStructure();
network.reset();
return network;
}
public static MLDataSet generateTraining()
{
final MLDataSet training = RandomTrainingFactory.generate(1000,50000,
INPUT_COUNT, OUTPUT_COUNT, -1, 1);
return training;
}
public static double evaluateRPROP(BasicNetwork network,MLDataSet data)
{
ResilientPropagation train = new ResilientPropagation(network,data);
train.setNumThreads(1);
long start = System.currentTimeMillis();
System.out.println("Training 20 Iterations with RPROP");
for(int i=1;i<=20;i++)
{
train.iteration();
System.out.println("Iteration #" + i + " Error:" + train.getError());
}
train.finishTraining();
long stop = System.currentTimeMillis();
double diff = ((double)(stop - start))/1000.0;
System.out.println("RPROP Result:" + diff + " seconds." );
System.out.println("Final RPROP error: " + network.calculateError(data));
return diff;
}
public static double evaluateMPROP(BasicNetwork network,MLDataSet data)
{
ResilientPropagation train = new ResilientPropagation(network,data);
train.setNumThreads(0);
long start = System.currentTimeMillis();
System.out.println("Training 20 Iterations with MPROP");
for(int i=1;i<=20;i++)
{
train.iteration();
System.out.println("Iteration #" + i + " Error:" + train.getError());
}
train.finishTraining();
long stop = System.currentTimeMillis();
double diff = ((double)(stop - start))/1000.0;
System.out.println("MPROP Result:" + diff + " seconds." );
System.out.println("Final MPROP error: " + network.calculateError(data));
return diff;
}
public static void main(String args[])
{
BasicNetwork network = generateNetwork();
MLDataSet data = generateTraining();
double rprop = evaluateRPROP(network,data);
double mprop = evaluateMPROP(network,data);
double factor = rprop/mprop;
System.out.println("Factor improvement:" + factor);
}
}