/*
* Copyright [2013-2014] PayPal Software Foundation
*
* 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.
*/
package ml.shifu.guagua.example.nn;
import java.util.Arrays;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.mathutil.error.ErrorCalculation;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.neural.error.ErrorFunction;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.networks.BasicNetwork;
/**
* {@link Gradient} is copied from Encog framework. The reason is that we original Gradient don't pop up
* {@link #gradients} outside. While we need gradients accumulated into {@link NNMaster} to update NN weights.
*/
public class Gradient {
/**
* The network to train.
*/
private FlatNetwork network;
/**
* The error calculation method.
*/
private final ErrorCalculation errorCalculation = new ErrorCalculation();
/**
* The actual values from the neural network.
*/
private final double[] actual;
/**
* The deltas for each layer.
*/
private final double[] layerDelta;
/**
* The neuron counts, per layer.
*/
private final int[] layerCounts;
/**
* The feed counts, per layer.
*/
private final int[] layerFeedCounts;
/**
* The layer indexes.
*/
private final int[] layerIndex;
/**
* The index to each layer's weights and thresholds.
*/
private final int[] weightIndex;
/**
* The output from each layer.
*/
private final double[] layerOutput;
/**
* The sums.
*/
private final double[] layerSums;
/**
* The gradients.
*/
private double[] gradients;
/**
* The weights and thresholds.
*/
private double[] weights;
/**
* The pair to use for training.
*/
private final MLDataPair pair;
/**
* The training data.
*/
private final MLDataSet training;
/**
* error
*/
private double error;
/**
* Derivative add constant. Used to combat flat spot.
*/
private double[] flatSpot;
/**
* The error function to use.
*/
private final ErrorFunction errorFunction;
/**
* Construct a gradient worker.
*
* @param theNetwork
* The network to train.
* @param theOwner
* The owner that is doing the training.
* @param theTraining
* The training data.
* @param theLow
* The low index to use in the training data.
* @param theHigh
* The high index to use in the training data.
*/
public Gradient(final FlatNetwork theNetwork, final MLDataSet theTraining, final double[] flatSpot, ErrorFunction ef) {
this.network = theNetwork;
this.training = theTraining;
this.flatSpot = flatSpot;
this.errorFunction = ef;
this.layerDelta = new double[getNetwork().getLayerOutput().length];
this.gradients = new double[getNetwork().getWeights().length];
this.actual = new double[getNetwork().getOutputCount()];
this.weights = getNetwork().getWeights();
this.layerIndex = getNetwork().getLayerIndex();
this.layerCounts = getNetwork().getLayerCounts();
this.weightIndex = getNetwork().getWeightIndex();
this.layerOutput = getNetwork().getLayerOutput();
this.layerSums = getNetwork().getLayerSums();
this.layerFeedCounts = getNetwork().getLayerFeedCounts();
this.pair = BasicMLDataPair.createPair(getNetwork().getInputCount(), getNetwork().getOutputCount());
}
/**
* Process one training set element.
*
* @param input
* The network input.
* @param ideal
* The ideal values.
* @param s
* The significance.
*/
private void process(final double[] input, final double[] ideal, double s) {
this.getNetwork().compute(input, this.actual);
this.errorCalculation.updateError(this.actual, ideal, s);
this.errorFunction.calculateError(ideal, actual, this.getLayerDelta());
for(int i = 0; i < this.actual.length; i++) {
this.getLayerDelta()[i] = ((this.getNetwork().getActivationFunctions()[0].derivativeFunction(
this.layerSums[i], this.layerOutput[i]) + this.flatSpot[0])) * (this.getLayerDelta()[i] * s);
}
for(int i = this.getNetwork().getBeginTraining(); i < this.getNetwork().getEndTraining(); i++) {
processLevel(i);
}
}
/**
* Process one level.
*
* @param currentLevel
* The level.
*/
private void processLevel(final int currentLevel) {
final int fromLayerIndex = this.layerIndex[currentLevel + 1];
final int toLayerIndex = this.layerIndex[currentLevel];
final int fromLayerSize = this.layerCounts[currentLevel + 1];
final int toLayerSize = this.layerFeedCounts[currentLevel];
final int index = this.weightIndex[currentLevel];
final ActivationFunction activation = this.getNetwork().getActivationFunctions()[currentLevel + 1];
final double currentFlatSpot = this.flatSpot[currentLevel + 1];
// handle weights
int yi = fromLayerIndex;
for(int y = 0; y < fromLayerSize; y++) {
final double output = this.layerOutput[yi];
double sum = 0;
int xi = toLayerIndex;
int wi = index + y;
for(int x = 0; x < toLayerSize; x++) {
this.gradients[wi] += output * this.getLayerDelta()[xi];
sum += this.weights[wi] * this.getLayerDelta()[xi];
wi += fromLayerSize;
xi++;
}
this.getLayerDelta()[yi] = sum
* (activation.derivativeFunction(this.layerSums[yi], this.layerOutput[yi]) + currentFlatSpot);
yi++;
}
}
/**
* Perform the gradient calculation
*/
public final void run() {
try {
// reset errors and gradients firstly
this.errorCalculation.reset();
Arrays.fill(this.gradients, 0.0);
for(int i = 0; i < this.training.getRecordCount(); i++) {
this.training.getRecord(i, this.pair);
process(this.pair.getInputArray(), this.pair.getIdealArray(), pair.getSignificance());
}
this.error = this.errorCalculation.calculate();
} catch (final Throwable ex) {
throw new RuntimeException(ex);
}
}
public ErrorCalculation getErrorCalculation() {
return errorCalculation;
}
/**
* @return the gradients
*/
public double[] getGradients() {
return this.gradients;
}
/**
* @return the error
*/
public double getError() {
return error;
}
/**
* @return the weights
*/
public double[] getWeights() {
return weights;
}
/**
* @param weights
* the weights to set
*/
public void setWeights(double[] weights) {
this.weights = weights;
this.getNetwork().setWeights(weights);
}
public void setParams(BasicNetwork network) {
this.network = network.getFlat();
this.weights = network.getFlat().getWeights();
}
public FlatNetwork getNetwork() {
return network;
}
public double[] getLayerDelta() {
return layerDelta;
}
}