MultiBench.java

/*
 * 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.
 *   
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 */
package org.encog.examples.neural.benchmark;

import org.encog.Encog;
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.setThreadCount(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.setThreadCount(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);
		Encog.getInstance().shutdown();
	}
}