coding_nn_init_param_init.py

def initialize_parameters_zeros(layers_dims):
    np.random.seed(1)               
    parameters = {}                 
    L = len(layers_dims)            

    for l in range(1, L):
        parameters["W" + str(l)] = np.zeros(
            (layers_dims[l], layers_dims[l - 1]))
        parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))

    return parameters


def initialize_parameters_random(layers_dims):
    np.random.seed(1)               
    parameters = {}                 
    L = len(layers_dims)            

    for l in range(1, L):
        parameters["W" + str(l)] = np.random.randn(
            layers_dims[l], layers_dims[l - 1]) * 10
        parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))

    return parameters


def initialize_parameters_he_xavier(layers_dims, initialization_method="he"):
    np.random.seed(1)               
    parameters = {}                 
    L = len(layers_dims)            

    if initialization_method == "he":
        for l in range(1, L):
            parameters["W" + str(l)] = np.random.randn(
                layers_dims[l],
                layers_dims[l - 1]) * np.sqrt(2 / layers_dims[l - 1])
            parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
    elif initialization_method == "xavier":
        for l in range(1, L):
            parameters["W" + str(l)] = np.random.randn(
                layers_dims[l],
                layers_dims[l - 1]) * np.sqrt(1 / layers_dims[l - 1])
            parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))

    return parameters