/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.mathutil.randomize;
import org.encog.engine.network.activation.ActivationReLU;
import org.encog.mathutil.matrices.Matrix;
import org.encog.mathutil.randomize.generate.GenerateRandom;
import org.encog.mathutil.randomize.generate.MersenneTwisterGenerateRandom;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
public class XaiverRandomizer implements Randomizer {
/**
* The y2 value.
*/
private double y2;
/**
* Should we use the last value.
*/
private boolean useLast = false;
private GenerateRandom rnd;
public XaiverRandomizer() {
this(System.currentTimeMillis());
}
public XaiverRandomizer(long seed) {
this.rnd = new MersenneTwisterGenerateRandom(seed);
}
/**
* Generate a random number.
*
* @param d
* The input value, not used.
* @return The random number.
*/
public double randomize(final double d) {
return this.rnd.nextDouble();
}
/**
* Randomize one level of a neural network.
*
* @param network
* The network to randomize
* @param fromLayer
* The from level to randomize.
*/
public void randomize(final BasicNetwork network,
final int fromLayer) {
final int fromCount = network.getLayerNeuronCount(fromLayer);
final int toCount = network.getLayerNeuronCount(fromLayer + 1);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++) {
// biases
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
network.setWeight(fromLayer, fromCount, toNeuron, 0);
}
// weights
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
double d;
if( network.getActivation(fromLayer) instanceof ActivationReLU) {
d = 2/Math.sqrt(fromCount);
} else {
d = 2/Math.sqrt((fromCount+toCount));
}
double w = this.rnd.nextDouble(-d,d);
network.setWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
/**
* Randomize the synapses and biases in the basic network based on an array,
* modify the array. Previous values may be used, or they may be discarded,
* depending on the randomizer.
*
* @param method
* A network to randomize.
*/
@Override
public void randomize(final MLMethod method) {
final BasicNetwork network = (BasicNetwork) method;
for (int i = 0; i < network.getLayerCount() - 1; i++) {
randomize(network, i);
}
}
@Override
public void randomize(double[] d) {
randomize(d,0,d.length);
}
@Override
public void randomize(double[][] d) {
for(int i=0;i<d.length;i++) {
for(int j=0;j<d[j].length;j++) {
d[i][j] = this.rnd.nextDouble();
}
}
}
@Override
public void randomize(Matrix m) {
randomize(m.getData());
}
@Override
public void randomize(double[] d, int begin, int size) {
for(int i=0;i<size;i++) {
d[begin+i] = this.rnd.nextDouble();
}
}
@Override
public void setRandom(GenerateRandom theRandom) {
this.rnd = theRandom;
}
@Override
public GenerateRandom getRandom() {
return this.rnd;
}
}