Single layer backward propagation step

single_layer_backward_propagation.py

def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation="relu"):
    m = A_prev.shape[1]
    
    if activation is "relu":
        backward_activation_func = relu_backward
    elif activation is "sigmoid":
        backward_activation_func = sigmoid_backward
    else:
        raise Exception('Non-supported activation function')
    
    dZ_curr = backward_activation_func(dA_curr, Z_curr)
    dW_curr = np.dot(dZ_curr, A_prev.T) / m
    db_curr = np.sum(dZ_curr, axis=1, keepdims=True) / m
    dA_prev = np.dot(W_curr.T, dZ_curr)

    return dA_prev, dW_curr, db_curr