Function softmax

mla/neuralnet/activations.py:13–16

(z)

def softmax(z):
    # Avoid numerical overflow by removing max
    e = np.exp(z - np.amax(z, axis=1, keepdims=True))
    return e / np.sum(e, axis=1, keepdims=True)


def linear(z):