function [W1, W2] = BackpropCE(W1, W2, X, D)

%Backpropagation with Cross Entropy cost function.

    alpha = 0.9;
    
    N = size(X,1);
    for k = 1:N
        x = X(k, :)'; % x = a column vector; input sample
        d = D(k);     %desired output
        
        v1 = W1*x;
        y1 = Sigmoid(v1);
        v = W2*y1;
        y = Sigmoid(v); %output of the neuron in the output layer.
        
        e = d - y;
        delta = e;
        
        e1 = W2'*delta;
        delta1 = y1.*(1-y1).*e1;
        dW1 = alpha*delta1*x';
        W1 = W1 + dW1;
        
        dW2 = alpha*delta*y1';
        W2 = W2 + dW2;
    end
end