coding_nn_reg_cost.py

def compute_cost_reg(AL, y, parameters, lambd=0):
    # number of examples
    m = y.shape[1]

    # compute traditional cross entropy cost
    cross_entropy_cost = compute_cost(AL, y)

    # convert parameters dictionary to vector
    parameters_vector = dictionary_to_vector(parameters)

    # compute the regularization penalty
    L2_regularization_penalty = (
        lambd / (2 * m)) * np.sum(np.square(parameters_vector))

    # compute the total cost
    cost = cross_entropy_cost + L2_regularization_penalty
    return cost