I'm trying to implement a neural network with:
- 5 input nodes(+1 bias)
- 1 hidden layer of 1 hidden node(+1 bias)
- 1 output unit.
The training data I'm using is the a disjunction of 5 input units. The Overall Error is oscillating instead of decreasing and reaching very high numbers.
package neuralnetworks; import java.io.File; import java.io.FileNotFoundException; import java.math.*; import java.util.Random; import java.util.Scanner; public class NeuralNetworks { private double[] weightslayer1; private double[] weightslayer2; private int[][] training; public NeuralNetworks(int inputLayerSize, int weights1, int weights2) { weightslayer1 = new double[weights1]; weightslayer2 = new double[weights2]; } public static int[][] readCSV() { Scanner readfile = null; try { readfile = new Scanner(new File("disjunction.csv")); } catch (FileNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } Scanner delimit; int[][] train = new int[32][6]; int lines = 0; while (readfile.hasNext()) { String line = readfile.nextLine(); delimit = new Scanner(line); delimit.useDelimiter(","); int features = 0; while (delimit.hasNext() && lines > 0) { train[lines - 1][features] = Integer.parseInt(delimit.next()); features++; } lines++; } return train; } public double linearcomb(double[] input, double[] weights) { //calculates the sum of the multiplication of weights and inputs double sigma = 0; for (int i = 0; i < input.length; i++) { sigma += (input[i] * weights[i]); } return sigma; } public double hiddenLayerOutput(int[] inputs) { //calculates the output of the hiddenlayer double[] formattedInput = new double[6]; //adds the bias unit formattedInput[0] = 1; for (int i = 1; i < formattedInput.length; i++) formattedInput[i] = inputs[i - 1]; double hlOutput = linearcomb(formattedInput, weightslayer1); return hlOutput; } public double feedForward(int[] inputs) { //calculates the output double hlOutput = hiddenLayerOutput(inputs); double[] olInput = new double[2]; olInput[0] = 1; olInput[1] = hlOutput; double output = linearcomb(olInput, weightslayer2); return output; } public void backprop(double predoutput, double targetout, double hidout, double learningrate, int[] input) { double outputdelta = predoutput * (1 - predoutput) * (targetout - predoutput); double hiddendelta = hidout * (1 - hidout) * (outputdelta * weightslayer2[1]); updateweights(learningrate, outputdelta, hiddendelta, input); } public void updateweights(double learningrate, double outputdelta, double hiddendelta, int[] input) { for (int i = 0; i < weightslayer1.length; i++) { double deltaw1 = learningrate * hiddendelta * input[i]; weightslayer1[i] += deltaw1; } for (int i = 0; i < weightslayer2.length; i++) { double deltaw2 = learningrate * outputdelta * hiddenLayerOutput(input); weightslayer2[i] += deltaw2; } } public double test(int[] inputs) { return feedForward(inputs); } public void train() { double learningrate = 0.01; double output; double hiddenoutput; double error = 100; do { error = 0; for (int i = 0; i < training.length; i++) { output = feedForward(training[i]); error += (training[i][5] - output) * (training[i][5] - output) / 2; hiddenoutput = hiddenLayerOutput(training[i]); backprop(output, training[i][5], hiddenoutput, learningrate, training[i]); } //System.out.println(error); }while(error>1); } public static void main(String[] args) { NeuralNetworks nn = new NeuralNetworks(6, 6, 2); Random rand = new Random(); nn.weightslayer2[0] = (rand.nextDouble() - 0.5); nn.weightslayer2[1] = (rand.nextDouble() - 0.5); for (int i = 0; i < nn.weightslayer1.length; i++) nn.weightslayer1[i] = (rand.nextDouble() - 0.5); nn.training = readCSV(); /*for (int i = 0; i < nn.training.length; i++) { for (int j = 0; j < nn.training[i].length; j++) System.out.print(nn.training[i][j] + ","); System.out.println(); }*/ nn.train(); int[] testa = { 0, 0, 0, 0, 0 }; System.out.println(nn.test(testa)); } } 
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