public NeuralNetwork(int input, int hidden, int output) {
this.layers = new int[] {input, hidden, output};
df = new DecimalFormat("#0.000#");
ef = new DecimalFormat("#");
/** Create all neurons and connections Connections are created in the neuron class */
for (int i = 0; i < layers.length; i++) {
if (i == 0) { // input layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
inputLayer.add(neuron);
}
} else if (i == 1) { // hidden layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
neuron.addInConnectionsS(inputLayer);
neuron.addBiasConnection(bias);
hiddenLayer.add(neuron);
}
} else if (i == 2) { // output layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
neuron.addInConnectionsS(hiddenLayer);
neuron.addBiasConnection(bias);
outputLayer.add(neuron);
}
} else {
System.out.println("!Error NeuralNetwork init");
}
}
// initialize random weights
for (Neuron neuron : hiddenLayer) {
ArrayList<Connection> connections = neuron.getAllInConnections();
for (Connection conn : connections) {
double newWeight = getRandom();
conn.setWeight(newWeight);
}
}
for (Neuron neuron : outputLayer) {
ArrayList<Connection> connections = neuron.getAllInConnections();
for (Connection conn : connections) {
double newWeight = getRandom();
conn.setWeight(newWeight);
}
}
// reset id counters
Neuron.counter = 0;
Connection.counter = 0;
if (isTrained) {
trainedWeights();
updateAllWeights();
}
}
/**
* 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());
}
}
}
/** Take from hash table and put into all weights */
public void updateAllWeights() {
// update weights for the output layer
for (Neuron n : outputLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
String key = weightKey(n.id, con.id);
double newWeight = weightUpdate.get(key);
con.setWeight(newWeight);
}
}
// update weights for the hidden layer
for (Neuron n : hiddenLayer) {
ArrayList<Connection> connections = n.getAllInConnections();
for (Connection con : connections) {
String key = weightKey(n.id, con.id);
double newWeight = weightUpdate.get(key);
con.setWeight(newWeight);
}
}
}
