""" Check that MNIST training/test data is functioning """ import src import mnist_training_binary # Create a list of 1000 training pairs. mnist_relations_train = mnist_training_binary.mnist_binary_relations('train') training_pairs = tuple(next(mnist_relations_train) for _ in range(1000)) # Display the 59th image. training_pairs[59][0].show('sparse') # Display the corresponding label. Can you see the digit in the above array? print(training_pairs[59][1]) print() # Create a list of 1000 test pairs. mnist_relations_test = mnist_training_binary.mnist_binary_relations('test') test_pairs = tuple(next(mnist_relations_test) for _ in range(1000)) # Display the 519th image. test_pairs[519][0].show('sparse') # Display the corresponding label. Can you see the digit in the above array? print(test_pairs[519][1]) print() # Create a list of 100 training pairs for use with a discrete neural net. zero_training_pairs = \ tuple(mnist_training_binary.binary_mnist_zero_one(100, 'train')) # This digit 0 is labeled with an all-black image (all ones) to indicate it is # a handwritten 0. zero_training_pairs[0][0]['x0'].show('sparse') zero_training_pairs[0][1][0].show('binary_pixels') print() # This digit 1 is labeled with an all-white image (all zeroes) to indicate it # is not a handwritten 0. zero_training_pairs[100][0]['x0'].show('sparse') zero_training_pairs[100][1][0].show('binary_pixels')