Neural network activations

def sigmoid(x):
    return 1 / (1 + numpy.exp(-x))


def tanh(x):
    return numpy.tanh(x)


def relu(x):
    return numpy.maximum(0, x)


def sigmoid_derivative(x):
    sig = sigmoid(x)
    return sig * (1 - sig)

def tanh_derivative(x):
    tan = tanh(x)
    return 1 - tan ** 2


def relu_derivative(x):
    y = numpy.zeros_like(x)
    y[x = 0] = 1
    return y