void run(int maxSteps, double minError) {
int i;
// Train neural network until minError reached or maxSteps exceeded
double error = 1;
for (i = 0; i < maxSteps && error > minError; i++) {
error = 0;
for (int p = 0; p < inputs.length; p++) {
setInput(inputs[p]);
activate();
output = getOutput();
resultOutputs[p] = output;
for (int j = 0; j < expectedOutputs[p].length; j++) {
double err = Math.pow(output[j] - expectedOutputs[p][j], 2);
error += err;
}
applyBackpropagation(expectedOutputs[p]);
}
}
printResult();
System.out.println("Sum of squared errors = " + error);
System.out.println("##### EPOCH " + i + "\n");
if (i == maxSteps) {
System.out.println("!Error training try again");
} else {
printAllWeights();
printWeightUpdate();
}
}
void test() {
setInput(testSet);
activate();
double[] outputtest = getOutput();
for (double temp : outputtest) {
System.out.println(temp);
}
}
public static void main(String[] args) throws Exception {
System.out.println("Testing Neural Network");
NeuralNetwork neuralNetwork = new NeuralNetwork(NUM_LAYERS, numLayerNodes, true);
System.out.println("--------------------------------------------------");
System.out.println("Testing initialisation of weights");
neuralNetwork.printWeights();
System.out.println("--------------------------------------------------");
System.out.println("Testing get weights");
Double[][][] weights = neuralNetwork.getWeights();
for (int i = 0; i < weights.length; i++) {
for (int l = 0; l < weights[i].length; l++) {
for (int k = 0; k < weights[i][l].length; k++) {
weights[i][l][k] = 9.0d;
}
}
}
System.out.println("--------------------------------------------------");
System.out.println("Testing set weights");
neuralNetwork.setWeights(weights);
neuralNetwork.printWeights();
System.out.println("--------------------------------------------------");
System.out.println("Testing set inputs");
Double[][][] inputs = new Double[NUM_LAYERS][][];
for (int i = 0; i < inputs.length; i++) {
Double[][] temp = new Double[numLayerNodes[i]][];
for (int l = 0; l < numLayerNodes[i]; l++) {
if (i == 0) {
temp[l] = new Double[1];
temp[l][0] = 0.5d;
} else {
temp[l] = new Double[inputs[i - 1].length];
for (int t = 0; t < temp[l].length; t++) {
temp[l][t] = 0.5d;
}
}
}
inputs[i] = temp;
}
neuralNetwork.setInput(inputs);
System.out.println("--------------------------------------------------");
System.out.println("Testing get Input");
inputs = neuralNetwork.getInputs();
for (int i = 0; i < inputs.length; i++) {
for (int l = 0; l < inputs[i].length; l++) {
for (int k = 0; k < inputs[i][l].length; k++) System.out.print(inputs[i][l][k] + ", ");
System.out.println();
}
System.out.println("_____________________________________________");
}
System.out.println("--------------------------------------------------");
System.out.println("Testing get Output");
Double[][] testInput = new Double[numLayerNodes[0]][1];
for (int i = 0; i < testInput.length; i++) {
testInput[i][0] = 1.0d - ((double) i / 10.0d);
System.out.print(testInput[i][0] + ", ");
}
System.out.println("\n\nFinal Output: ");
Double[] output = neuralNetwork.getOutput(testInput);
for (int i = 0; i < output.length; i++) {
System.out.print(output[i] + "; ");
}
}
