Function forward

ann_class/xor_donut.py:19–32

(X, W1, b1, W2, b2)

# for binary classification! no softmax here

def forward(X, W1, b1, W2, b2):
    # sigmoid
    # Z = 1 / (1 + np.exp( -(X.dot(W1) + b1) ))

    # tanh
    # Z = np.tanh(X.dot(W1) + b1)

    # relu
    Z = X.dot(W1) + b1
    Z = Z * (Z > 0)

    activation = Z.dot(W2) + b2
    Y = 1 / (1 + np.exp(-activation))
    return Y, Z


def predict(X, W1, b1, W2, b2):