def model(X, Y, layers_dims, learning_rate=0.01, num_iterations=1000,
          print_cost=True, hidden_layers_activation_fn="relu",
          initialization_method="he"):
    np.random.seed(1)

    # initialize cost list
    cost_list = []

    # initialize parameters
    if initialization_method == "zeros":
        parameters = initialize_parameters_zeros(layers_dims)
    elif initialization_method == "random":
        parameters = initialize_parameters_random(layers_dims)
    else:
        parameters = initialize_parameters_he_xavier(
            layers_dims, initialization_method)

    # iterate over num_iterations
    for i in range(num_iterations):
        # iterate over L-layers to get the final output and the cache
        AL, caches = L_model_forward(
            X, parameters, hidden_layers_activation_fn)

        # compute cost to plot it
        cost = compute_cost(AL, Y)

        # iterate over L-layers backward to get gradients
        grads = L_model_backward(AL, Y, caches, hidden_layers_activation_fn)

        # update parameters
        parameters = update_parameters(parameters, grads, learning_rate)

        # append each 100th cost to the cost list
        if (i + 1) % 100 == 0 and print_cost:
            print("The cost after {} iterations is: {}".format(i + 1, cost))

        if i % 100 == 0:
            cost_list.append(cost)

    # plot the cost curve
    plt.figure(figsize=(12, 8))
    plt.plot(cost_list)
    plt.xlabel("Iterations (per hundreds)", fontsize=14)
    plt.ylabel("Cost", fontsize=14)
    plt.title(
        "Cost curve: learning rate = {} and {} initialization method".format(
            learning_rate, initialization_method), y=1.05, fontsize=16)

    return parameters