/**
* all output propagate back
*
* @param expectedOutput first calculate the partial derivative of the error with respect to each
* of the weight leading into the output neurons bias is also updated here
*/
public void applyBackpropagation(double expectedOutput[]) {
// error check, normalize value ]0;1[
for (int i = 0; i < expectedOutput.length; i++) {
double d = expectedOutput[i];
if (d < 0 || d > 1) {
if (d < 0) expectedOutput[i] = 0 + epsilon;
else expectedOutput[i] = 1 - epsilon;
}
}
int i = 0;
for (Neuron n : outputLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
double ak = n.getOutput();
double ai = con.leftNeuron.getOutput();
double desiredOutput = expectedOutput[i];
double partialDerivative = -ak * (1 - ak) * ai * (desiredOutput - ak);
double deltaWeight = -learningRate * partialDerivative;
double newWeight = con.getWeight() + deltaWeight;
con.setDeltaWeight(deltaWeight);
con.setWeight(newWeight + momentum * con.getPrevDeltaWeight());
}
i++;
}
// update weights for the hidden layer
for (Neuron n : hiddenLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
double aj = n.getOutput();
double ai = con.leftNeuron.getOutput();
double sumKoutputs = 0;
int j = 0;
for (Neuron out_neu : outputLayer) {
double wjk = out_neu.getConnection(n.id).getWeight();
double desiredOutput = (double) expectedOutput[j];
double ak = out_neu.getOutput();
j++;
sumKoutputs = sumKoutputs + (-(desiredOutput - ak) * ak * (1 - ak) * wjk);
}
double partialDerivative = aj * (1 - aj) * ai * sumKoutputs;
double deltaWeight = -learningRate * partialDerivative;
double newWeight = con.getWeight() + deltaWeight;
con.setDeltaWeight(deltaWeight);
con.setWeight(newWeight + momentum * con.getPrevDeltaWeight());
}
}
}
public void printWeightUpdate() {
String fileName = "WeightFile.txt";
try {
FileWriter fileWriter = new FileWriter(fileName);
BufferedWriter bufferedWriter = new BufferedWriter(fileWriter);
System.out.println(
"printWeightUpdate, put this i trainedWeights() and set isTrained to true");
// weights for the hidden layer
for (Neuron n : hiddenLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
String w = df.format(con.getWeight());
System.out.println(
"weightUpdate.put(weightKey(" + n.id + ", " + con.id + "), " + w + ");");
bufferedWriter.write(ef.format(n.id));
bufferedWriter.write(" ");
bufferedWriter.write(ef.format(con.id));
bufferedWriter.write(" ");
bufferedWriter.write(w);
bufferedWriter.newLine();
}
}
// weights for the output layer
for (Neuron n : outputLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
String w = df.format(con.getWeight());
System.out.println(
"weightUpdate.put(weightKey(" + n.id + ", " + con.id + "), " + w + ");");
bufferedWriter.write(ef.format(n.id));
bufferedWriter.write(" ");
bufferedWriter.write(ef.format(con.id));
bufferedWriter.write(" ");
bufferedWriter.write(w);
bufferedWriter.newLine();
}
}
System.out.println();
bufferedWriter.close();
} catch (IOException ex) {
System.out.println("Error writing to file " + fileName);
}
}
public void printAllWeights() {
System.out.println("printAllWeights");
// weights for the hidden layer
for (Neuron n : hiddenLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
double w = con.getWeight();
System.out.println("n=" + n.id + " c=" + con.id + " w=" + w);
}
}
// weights for the output layer
for (Neuron n : outputLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
double w = con.getWeight();
System.out.println("n=" + n.id + " c=" + con.id + " w=" + w);
}
}
System.out.println();
}
private void addNode() {
int connectionIndex = this.randomGenerator.nextInt(this.connections.size());
Connection connection = this.connections.get(connectionIndex);
connection.disable();
int nodeId =
(this.hiddenNodes.isEmpty()
? this.outputNodes.get(this.outputNodes.size() - 1).getId()
: this.hiddenNodes.get(this.hiddenNodes.size() - 1).getId())
+ 1;
this.hiddenNodes.add(new Node(nodeId, NodeType.HIDDEN));
this.connections.add(new Connection(connection.getFrom(), nodeId, 1));
this.connections.add(new Connection(nodeId, connection.getTo(), connection.getWeight()));
}
private void mutateWeights() {
for (Connection connection : this.connections) {
if (this.randomGenerator.nextDouble() > 0.8) {
// 80% chance of mutation
continue;
}
if (this.randomGenerator.nextDouble() > 0.9) {
// 10% chance of random reset
connection.setWeight(NNGenome.scale(this.randomGenerator.nextDouble(), 0, 1, -1, 1));
} else { // 90% chance of perturbation
double perturbation = NNGenome.scale(this.randomGenerator.nextDouble(), 0, 1, -0.01, 0.01);
connection.setWeight(connection.getWeight() + perturbation);
}
}
}