public void train() {
for (int i = 0; i < weights.getM(); ++i) {
for (int j = 0; j < weights.getN(); ++j) {
weights.set(i, j, weights.get(i, j) + (learning * inputs.get(i) * errors.get(j)));
}
}
}
public Vector backProp(Vector error) {
derivs = fcn.applyDerivative(outputs);
errors = derivs.multiply(error);
Vector blame = weights.transpose().multiply(errors);
// take off the bias signal, the previous layer need not know
return blame.slice(blame.dim() - 1);
}
public Vector output(Vector inputs) {
Vector in = inputs.append(bias);
this.inputs = in;
in = weights.multiply(in);
outputs = fcn.apply(in);
return outputs;
}
