/*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.plugins.opencl.example;
import java.util.ArrayList;
import java.util.List;
import org.encog.Encog;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.flat.train.prop.TrainFlatNetworkBackPropagation;
import org.encog.plugins.opencl.EncogOpenCLPlugin;
import org.encog.util.Format;
import org.encog.util.Stopwatch;
public class OpenCLBenchmark {
public static final int ROW_COUNT = 100;
public static final int INPUT_COUNT = 10;
public static final int OUTPUT_COUNT = 1;
public static final int HIDDEN_COUNT = 200;
public static final int ITERATIONS = 10;
public static final int AVG_COUNT = 20;
public static long benchmarkEncogFlat(double[][] input, double[][] output) {
FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
output[0].length, false);
network.randomize();
BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);
TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
network, trainingSet, 0.7, 0.7);
double[] a = new double[2];
double[] b = new double[1];
Stopwatch sw = new Stopwatch();
sw.start();
// run epoch of learning procedure
for (int i = 0; i < ITERATIONS; i++) {
train.iteration();
}
sw.stop();
return sw.getElapsedMilliseconds();
}
static double[][] generate(int rows, int columns) {
double[][] result = new double[rows][columns];
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
result[i][j] = Math.random();
}
}
return result;
}
public static void main(String[] args) {
Encog.getInstance().registerPlugin(new EncogOpenCLPlugin());
// initialize input and output values
double[][] input = generate(ROW_COUNT, INPUT_COUNT);
double[][] output = generate(ROW_COUNT, OUTPUT_COUNT);
List<Long> previous = new ArrayList<Long>();
for(;;) {
long time = benchmarkEncogFlat(input, output);
previous.add(time);
StringBuilder line = new StringBuilder();
line.append("Time: ");
line.append(Format.formatInteger((int)time));
if( previous.size()<=AVG_COUNT ) {
line.append(", no average yet");
} else {
previous.remove(0);
long avg = 0;
for(long l: previous) {
avg+=l;
}
line.append(", average over last ");
line.append(previous.size());
line.append(" is ");
line.append(avg/previous.size());
}
System.out.println(line.toString());
}
}
}