Perceptron (Java)

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[edit] Overview

A simple, illustrative implementation of a single-layered perceptron in Java. When a pattern is impressed on the perceptron the activation of the network is adjusted according to an activiation formula and a given bias value. To adjust the way the perceptron reacts to a given input, a learning algorithm (the delta rule) is implemented to adjust the weights connecting the neurons of the perceptron, which are initially set to 0. This rule keeps adjusting the weights until the resulting output for a given input corresponds to a supplied correct output, resulting in a perceptron trained to react to a certain perception in a certain way. The input patterns and the teaching output are hard coded as integer matrices.

[edit] Implementation

  • The patterns to be impressed on the input layer of the perceptron:
<<patterns>>=

int[][] patterns = { 
    { 0, 0, 0, 0 }, 
    { 0, 0, 0, 1 }, 
    { 0, 0, 1, 0 },
    { 0, 0, 1, 1 }, 
    { 0, 1, 0, 0 }, 
    { 0, 1, 0, 1 }, 
    { 0, 1, 1, 0 },
    { 0, 1, 1, 1 }, 
    { 1, 0, 0, 0 }, 
    { 1, 0, 0, 1 } };
  • The teaching output for the perceptron, the desired reaction or learning goal:
<<teaching>>=

int[][] teachingOutput = { 
    { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
    { 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, 
    { 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 },
    { 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 }, 
    { 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 },
    { 1, 1, 1, 1, 1, 0, 0, 0, 0, 0 }, 
    { 1, 1, 1, 1, 1, 1, 0, 0, 0, 0 },
    { 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 }, 
    { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 },
    { 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 } };

The number of input and output neurons and the number of patterns depend on these matrices. The weights of the connections between neurons in the perceptron are represented in a matrix of doubles. The constructor initializes the weights with the given numbers of input and output neurons.

<<attributes>>=

int numberOfInputNeurons = patterns[0].length;
int numberOfOutputNeurons = teachingOutput.length;
int numberOfPatterns = patterns.length;
double[][] weights;

public Perceptron() {
    weights = new double[numberOfInputNeurons][numberOfOutputNeurons];
}

The delta rule is the learning algorithm for adjusting the weights of the perceptron given an input value, a desired output and an untrained perceptron. The output of the perceptron is compared to the desired output and weights are adjusted if an error is found, where an error is a difference between the actual output and the expected output (the teaching output).

<<delta>>=

int[] output = setOutputValues(i);
for (int j = 0; j < numberOfOutputNeurons; j++) {
    if (teachingOutput[i][j] != output[j]) {
        for (int k = 0; k < numberOfInputNeurons; k++) {
            weights[k][j] = weights[k][j] + learningFactor
                    * patterns[i][k]
                    * (teachingOutput[i][j] - output[j]);
        }
    }
}
for (int z = 0; z < output.length; z++) {
    if (output[z] != teachingOutput[i][z])
        error = true;
}

Impress a pattern on the input layer and set the output layer for the applied pattern. The method takes one parameter: the index of the pattern to apply and returns an array of integers: the resulting output, the reaction. Depending on the result of the activation formula and the bias a neuron is activated or deactivated by setting the value of the corresponding position to 1 or 0.

<<impress>>=

for (int j = 0; j < result.length; j++) {
    double net = weights[0][j] * toImpress[0] + weights[1][j]
            * toImpress[1] + weights[2][j] * toImpress[2]
            + weights[3][j] * toImpress[3];
    if (net > bias)
        result[j] = 1;
    else
        result[j] = 0;
}

A util method for printing a matrix of doubles formatting the values to one decimal digit (using DecimalFormat) to get a readable output of weights, representing the connections of neurons in the perceptron.

<<print_matrix>>=

for (int i = 0; i < matrix.length; i++) {
    for (int j = 0; j < matrix[i].length; j++) {
        NumberFormat f = NumberFormat.getInstance();
        if (f instanceof DecimalFormat) {
            DecimalFormat decimalFormat = ((DecimalFormat) f);
            decimalFormat.setMaximumFractionDigits(1);
            decimalFormat.setMinimumFractionDigits(1);
            System.out.print("(" + f.format(matrix[i][j]) + ")");
        }
    }
    System.out.println();
}

[edit] Usage

  • Demonstration of the usage in a JUnit 4 unit test (requires Java 5).
<<usage>>=

Perceptron p = new Perceptron();
System.out.println("Weights before training: ");
p.printMatrix(p.weights);
p.deltaRule();
System.out.println("Weights after training: ");
p.printMatrix(p.weights);
  • The complete program:
<<Perceptron.java>>=

import java.text.DecimalFormat;
import java.text.NumberFormat;

import org.junit.Test;

public class Perceptron {
	patterns
	teaching
	attributes
	public void deltaRule() {
		boolean allCorrect = false;
		boolean error = false;
		double learningFactor = 0.2;
		while (!allCorrect) {
			error = false;
			for (int i = 0; i < numberOfPatterns; i++) {
				delta
			 }
			if (!error) {
				allCorrect = true;
			}
		}
	}
	
	int[] setOutputValues(int patternNo) {
		double bias = 0.7;
		int[] result = new int[numberOfOutputNeurons];
		int[] toImpress = patterns[patternNo];
		for (int i = 0; i < toImpress.length; i++) {
			impress
		}
		return result;
	}
	
	public void printMatrix(double[][] matrix) {
		print_matrix
	}
	
	@Test
	public void testPerceptron() {
		usage
	}
}
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