Final Model.py

def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
    """
    Builds the logistic regression model by calling the function you've implemented previously
    
    Arguments:
    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
    print_cost -- Set to True to print the cost every 100 iterations
    
    Returns:
    d -- dictionary containing information about the model.
    """
    # (≈ 1 line of code)   
    # initialize parameters with zeros 
    # w, b = ...
    
    #(≈ 1 line of code)
    # Gradient descent 
    # params, grads, costs = ...
    
    # Retrieve parameters w and b from dictionary "params"
    # w = ...
    # b = ...
    
    # Predict test/train set examples (≈ 2 lines of code)
    # Y_prediction_test = ...
    # Y_prediction_train = ...
    
    # YOUR CODE STARTS HERE
    w, b=initialize_with_zeros(X_train.shape[0])
    params, grads, costs =optimize(w, b, X_train,Y_train,num_iterations,learning_rate, print_cost)
    w=params['w']
    b=params['b']
    Y_prediction_test=predict(w,b,X_test)
    Y_prediction_train=predict(w,b,X_train)
    
    # YOUR CODE ENDS HERE

    # Print train/test Errors
    if print_cost:
        print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
        print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))

    
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    
    return d