/*
* Encog(tm) Core 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.util.benchmark;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.simple.EncogUtility;
/**
* Used to evaluate the training time for a network.
*
* @author jheaton
*
*/
public final class Evaluate {
/**
* Mili-seconds in a second.
*/
public static final int MILIS = 1000;
public static int evaluateTrain(int input, int hidden1, int hidden2,
int output) {
final BasicNetwork network = EncogUtility.simpleFeedForward(input,
hidden1, hidden2, output, true);
final MLDataSet training = RandomTrainingFactory.generate(1000,
10000, input, output, -1, 1);
return evaluateTrain(network, training);
}
/**
* Evaluate how long it takes to calculate the error for the network. This
* causes each of the training pairs to be run through the network. The
* network is evaluated 10 times and the lowest time is reported.
*
* @param network
* The network to evaluate with.
* @param training
* The training data to use.
* @return The lowest number of seconds that each of the ten attempts took.
*/
public static int evaluateTrain(
final BasicNetwork network, final MLDataSet training) {
// train the neural network
MLTrain train;
train = new ResilientPropagation(network, training);
final long start = System.currentTimeMillis();
final long stop = start + (10 * MILIS);
int iterations = 0;
while (System.currentTimeMillis() < stop) {
iterations++;
train.iteration();
}
return iterations;
}
}