def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):     """     Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.     Arguments:     X -- input data, of shape (n_x, number of examples)     Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)     layers_dims -- dimensions of the layers (n_x, n_h, n_y)     num_iterations -- number of iterations of the optimization loop     learning_rate -- learning rate of the gradient descent update rule     print_cost -- If set to True, this will print the cost every 100 iterations     Returns:     parameters -- a dictionary containing W1, W2, b1, and b2     """     np.random.seed(1)     grads = {}     costs = []                              # to keep track of the cost     m = X.shape[1]                           # number of examples     (n_x, n_h, n_y) = layers_dims     # Initialize parameters dictionary, by calling one of the functions you'd previously implemented     ### START CODE HERE ### (≈ 1 line of code)     parameters = initialize_parameters(n_x, n_h, n_y)     ### END CODE HERE ###     # Get W1, b1, W2 and b2 from the dictionary parameters.     W1 = parameters["W1"]     b1 = parameters["b1"]     W2 = parameters["W2"]     b2 = parameters["b2"]     # Loop (gradient descent)     for i in range(0, num_iterations):         # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: "X, W1, b1". Output: "A1, cache1, A2, cache2".         ### START CODE HERE ### (≈ 2 lines of code)         A1, cache1 = linear_activation_forward(X, W1, b1, 'relu')         A2, cache2 = linear_activation_forward(A1, W2, b2, 'sigmoid')         ### END CODE HERE ###         # Compute cost         ### START CODE HERE ### (≈ 1 line of code)         cost = compute_cost(A2, Y)         ### END CODE HERE ###         # Initializing backward propagation         dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))         # Backward propagation. Inputs: "dA2, cache2, cache1". Outputs: "dA1, dW2, db2; also dA0 (not used), dW1, db1".         ### START CODE HERE ### (≈ 2 lines of code)         dA1, dW2, db2 = linear_activation_backward(dA2, cache2, 'sigmoid')         dA0, dW1, db1 = linear_activation_backward(dA1, cache1, 'relu')         ### END CODE HERE ###         # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2         grads['dW1'] = dW1         grads['db1'] = db1         grads['dW2'] = dW2         grads['db2'] = db2         # Update parameters.         ### START CODE HERE ### (approx. 1 line of code)         parameters = update_parameters(parameters, grads, learning_rate)         ### END CODE HERE ###         # Retrieve W1, b1, W2, b2 from parameters         W1 = parameters["W1"]         b1 = parameters["b1"]         W2 = parameters["W2"]         b2 = parameters["b2"]         # Print the cost every 100 training example         if print_cost and i % 100 == 0:             print("Cost after iteration {}: {}".format(i, np.squeeze(cost)))         if print_cost and i % 100 == 0:             costs.append(cost)     # plot the cost     plt.plot(np.squeeze(costs))     plt.ylabel('cost')     plt.xlabel('iterations (per tens)')     plt.title("Learning rate =" + str(learning_rate))     plt.show()     return parameters