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Further Study
This program is only a basic introduction to neural network programming. I have used the network class presented in this article for a number of more complex tasks, ranging from optical character recognition to recognizing patterns in Web pages. If you want to learn more about neural networks, you may want to check out the following sites.
http://joone.sourceforge.net: JOONE, an open source neural network library for Java.
http://www.jeffheaton.com/ai: Other example programs that I have written for AI, including some information on optical character recognition (OCR).
Listing 1—A Reusable Single Hidden Layer Neural Network
/**
* Neural Network
* Feedforward Backpropagation Neural Network
* Written in 2002 by Jeff Heaton(http://www.jeffheaton.com)
*
* This class is released under the limited GNU public
* license (LGPL).
*
* @author Jeff Heaton
* @version 1.0
*/
public class Network {
/**
* The global error for the training.
*/
protected double globalError;
/**
* The number of input neurons.
*/
protected int inputCount;
/**
* The number of hidden neurons.
*/
protected int hiddenCount;
/**
* The number of output neurons
*/
protected int outputCount;
/**
* The total number of neurons in the network.
*/
protected int neuronCount;
/**
* The number of weights in the network.
*/
protected int weightCount;
/**
* The learning rate.
*/
protected double learnRate;
/**
* The outputs from the various levels.
*/
protected double fire[];
/**
* The weight matrix this, along with the thresholds can be
* thought of as the "memory" of the neural network.
*/
protected double matrix[];
/**
* The errors from the last calculation.
*/
protected double error[];
/**
* Accumulates matrix delta's for training.
*/
protected double accMatrixDelta[];
/**
* The thresholds, this value, along with the weight matrix
* can be thought of as the memory of the neural network.
*/
protected double thresholds[];
/**
* The changes that should be applied to the weight
* matrix.
*/
protected double matrixDelta[];
/**
* The accumulation of the threshold deltas.
*/
protected double accThresholdDelta[];
/**
* The threshold deltas.
*/
protected double thresholdDelta[];
/**
* The momentum for training.
*/
protected double momentum;
/**
* The changes in the errors.
*/
protected double errorDelta[];
/**
* Construct the neural network.
*
* @param inputCount The number of input neurons.
* @param hiddenCount The number of hidden neurons
* @param outputCount The number of output neurons
* @param learnRate The learning rate to be used when training.
* @param momentum The momentum to be used when training.
*/
public Network(int inputCount,
int hiddenCount,
int outputCount,
double learnRate,
double momentum) {
this.learnRate = learnRate;
this.momentum = momentum;
this.inputCount = inputCount;
this.hiddenCount = hiddenCount;
this.outputCount = outputCount;
neuronCount = inputCount + hiddenCount + outputCount;
weightCount = (inputCount * hiddenCount) + (hiddenCount * outputCount);
fire = new double[neuronCount];
matrix = new double[weightCount];
matrixDelta = new double[weightCount];
thresholds = new double[neuronCount];
errorDelta = new double[neuronCount];
error = new double[neuronCount];
accThresholdDelta = new double[neuronCount];
accMatrixDelta = new double[weightCount];
thresholdDelta = new double[neuronCount];
reset();
}
/**
* Returns the root mean square error for a complet training set.
*
* @param len The length of a complete training set.
* @return The current error for the neural network.
*/
public double getError(int len) {
double err = Math.sqrt(globalError / (len * outputCount));
globalError = 0; // clear the accumulator
return err;
}
/**
* The threshold method. You may wish to override this class to provide other
* threshold methods.
*
* @param sum The activation from the neuron.
* @return The activation applied to the threshold method.
*/
public double threshold(double sum) {
return 1.0 / (1 + Math.exp(-1.0 * sum));
}
/**
* Compute the output for a given input to the neural network.
*
* @param input The input provide to the neural network.
* @return The results from the output neurons.
*/
public double []computeOutputs(double input[]) {
int i, j;
final int hiddenIndex = inputCount;
final int outIndex = inputCount + hiddenCount;
for (i = 0; i < inputCount; i++) {
fire[i] = input[i];
}
// first layer
int inx = 0;
for (i = hiddenIndex; i < outIndex; i++) {
double sum = thresholds[i];
for (j = 0; j < inputCount; j++) {
sum += fire[j] * matrix[inx++];
}
fire[i] = threshold(sum);
}
// hidden layer
double result[] = new double[outputCount];
for (i = outIndex; i < neuronCount; i++) {
double sum = thresholds[i];
for (j = hiddenIndex; j < outIndex; j++) {
sum += fire[j] * matrix[inx++];
}
fire[i] = threshold(sum);
result[i-outIndex] = fire[i];
}
return result;
}
/**
* Calculate the error for the recogntion just done.
*
* @param ideal What the output neurons should have yielded.
*/
public void calcError(double ideal[]) {
int i, j;
final int hiddenIndex = inputCount;
final int outputIndex = inputCount + hiddenCount;
// clear hidden layer errors
for (i = inputCount; i < neuronCount; i++) {
error[i] = 0;
}
// layer errors and deltas for output layer
for (i = outputIndex; i < neuronCount; i++) {
error[i] = ideal[i - outputIndex] - fire[i];
globalError += error[i] * error[i];
errorDelta[i] = error[i] * fire[i] * (1 - fire[i]);
}
// hidden layer errors
int winx = inputCount * hiddenCount;
for (i = outputIndex; i < neuronCount; i++) {
for (j = hiddenIndex; j < outputIndex; j++) {
accMatrixDelta[winx] += errorDelta[i] * fire[j];
error[j] += matrix[winx] * errorDelta[i];
winx++;
}
accThresholdDelta[i] += errorDelta[i];
}
// hidden layer deltas
for (i = hiddenIndex; i < outputIndex; i++) {
errorDelta[i] = error[i] * fire[i] * (1 - fire[i]);
}
// input layer errors
winx = 0; // offset into weight array
for (i = hiddenIndex; i < outputIndex; i++) {
for (j = 0; j < hiddenIndex; j++) {
accMatrixDelta[winx] += errorDelta[i] * fire[j];
error[j] += matrix[winx] * errorDelta[i];
winx++;
}
accThresholdDelta[i] += errorDelta[i];
}
}
/**
* Modify the weight matrix and thresholds based on the last call to
* calcError.
*/
public void learn() {
int i;
// process the matrix
for (i = 0; i < matrix.length; i++) {
matrixDelta[i] = (learnRate * accMatrixDelta[i]) + (momentum * matrixDelta[i]);
matrix[i] += matrixDelta[i];
accMatrixDelta[i] = 0;
}
// process the thresholds
for (i = inputCount; i < neuronCount; i++) {
thresholdDelta[i] = learnRate * accThresholdDelta[i] + (momentum * thresholdDelta[i]);
thresholds[i] += thresholdDelta[i];
accThresholdDelta[i] = 0;
}
}
/**
* Reset the weight matrix and the thresholds.
*/
public void reset() {
int i;
for (i = 0; i < neuronCount; i++) {
thresholds[i] = 0.5 - (Math.random());
thresholdDelta[i] = 0;
accThresholdDelta[i] = 0;
}
for (i = 0; i < matrix.length; i++) {
matrix[i] = 0.5 - (Math.random());
matrixDelta[i] = 0;
accMatrixDelta[i] = 0;
}
}
}
Listing 2—Testing the Neural Network on the XOR Problem
import java.text.*;
public class TestNeuralNetwork {
public static void main(String args[])
{
double xorInput[][] =
{
{0.0,0.0},
{1.0,0.0},
{0.0,1.0},
{1.0,1.0}};
double xorIdeal[][] =
{ {0.0},{1.0},{1.0},{0.0}};
System.out.println("Learn:");
Network network = new Network(2,3,1,0.7,0.9);
NumberFormat percentFormat = NumberFormat.getPercentInstance();
percentFormat.setMinimumFractionDigits(4);
for (int i=0;i<10000;i++) {
for (int j=0;j<xorInput.length;j++) {
network.computeOutputs(xorInput[j]);
network.calcError(xorIdeal[j]);
network.learn();
}
System.out.println( "Trial #" + i + ",Error:" +
percentFormat .format(network.getError(xorInput.length)) );
}
System.out.println("Recall:");
for (int i=0;i<xorInput.length;i++) {
for (int j=0;j<xorInput[0].length;j++) {
System.out.print( xorInput[i][j] +":" );
}
double out[] = network.computeOutputs(xorInput[i]);
System.out.println("="+out[0]);
}
}
}
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