@Override
public double value(final Vec mu) {
double result = 0.0;
for (int i = 0; i < ds.length(); i++) {
final double trans = binClassifier.value(ds.data().row(i));
final double sigmoid = MathTools.sigmoid(trans);
final double underLog =
mu.get(target.label(i)) * sigmoid + (1 - mu.get(target.label(i))) * (1 - sigmoid);
result -= Math.log(underLog);
}
result += c * VecTools.multiply(MxTools.multiply(laplacian, mu), mu);
return result;
}
public void backward() {
Mx cnc = null;
if (bias_b != 0) {
cnc = leftContract(output);
} else {
cnc = VecTools.copy(output);
}
difference = MxTools.multiply(MxTools.transpose(cnc), activations);
for (int i = 0; i < difference.dim(); i++) {
difference.set(i, difference.get(i) / activations.rows());
}
input = MxTools.multiply(cnc, weights);
rectifier.grad(activations, activations);
for (int i = 0; i < input.dim(); i++) {
input.set(i, input.get(i) * activations.get(i));
if (dropoutFraction > 0) {
input.set(i, input.get(i) * dropoutMask.get(i));
}
}
}
public void forward() {
if (bias != 0) {
activations = leftExtend(input);
} else {
activations = VecTools.copy(input);
}
output = MxTools.multiply(activations, MxTools.transpose(weights));
rectifier.value(output, output);
if (dropoutFraction > 0) {
if (isTrain) {
dropoutMask = getDropoutMask();
for (int i = 0; i < output.dim(); i++) {
output.set(i, output.get(i) * dropoutMask.get(i));
}
} else {
for (int i = 0; i < output.dim(); i++) {
output.set(i, output.get(i) * (1 - dropoutFraction));
}
}
}
}