Function forward

ann_class/backprop.py:17–22

(X, W1, b1, W2, b2)

np.random.seed(1)

def forward(X, W1, b1, W2, b2):
    Z = 1 / (1 + np.exp(-X.dot(W1) - b1))
    A = Z.dot(W2) + b2
    expA = np.exp(A)
    Y = expA / expA.sum(axis=1, keepdims=True)
    return Y, Z


# determine the classification rate