text: ############################################
  
  # This is an attempt to fully understand the 
  # basics of CNNs using MNIST data.I will try
  # to include more than one type of model so
  # that I can improve my ability to structure
  # a repository. 
  # 
  # Please see the file "MNIST_functions.py"
  # for functions such as "print_help()" which
  # are helpful along the way. 
  # 
  # This repository is the unique work of 
  # William Lindskog, December 2021.  
  
  ############################################
  
  #########################################
  ##### ----- ADD PACKAGES HERE ----- #####
  #########################################
  from MNIST_functions import *
  
  ############################################
  ##### ----- BEGIN SOLVING MNIST ----- ######
  ############################################
  
  ##### ----- LOAD DATA SET ----- ######
  train_X, train_y, test_X, test_y = load_dataset()
  
  ##### ----- PREPARE PIXELS ----- #####
  train_X, train_y, test_X, test_y = prepare_pixels(train_X), prepare_pixels(train_y), prepare_pixels(test_X), prepare_pixels(test_y)
  
  ##### ----- DEFINE MODEL ----- #####
  # model = define_model()
  
  ##### ----- COMPILE MODEL ----- #####
  # model = compile_model(model)
  
  N_FOLDS = 5
  evaluate_model(train_X, train_y, N_FOLDS)
  quit()