def test_returns_non_uniform_array_after_training(self):
x = [np.array([0] * 784, float)]
y = [np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], float)]
pixels_to_categories = (x, y)
diggen = DigitGenerator()
diggen.train(pixels_to_categories=pixels_to_categories)
pixels = diggen.generate_digit(digit=1)
expected_pixels = np.zeros(784, dtype=np.uint8)
expected_pixels.fill(127)
self.assertNotEqual(pixels.tolist(), expected_pixels.tolist())
def test_for_single_example(self):
x = [np.array([9, 124, 203], float)]
y = [np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], float)]
pixels_to_categories = (x, y)
X, Y = DigitGenerator.prepare_train_examples(pixels_to_categories)
self.assertEqual(X[0].tolist(), y[0].tolist())
self.assertEqual(Y[0].tolist(), [9 / 255.0, 124 / 255.0, 203 / 255.0])
x = [np.array([55, 2], float)]
y = [np.array([0.1, 0.8, 0, 0, 0, 0, 0, 0, 0, 0], float)]
pixels_to_categories = (x, y)
X, Y = DigitGenerator.prepare_train_examples(pixels_to_categories)
self.assertEqual(X[0].tolist(), y[0].tolist())
self.assertEqual(Y[0].tolist(), [55 / 255.0, 2 / 255.0])
def test_can_only_generate_digits_from_0_to_9(self):
diggen = DigitGenerator()
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=-1))
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=-5))
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=-50))
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=10))
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=50))
def step(context):
mnist.download_dataset()
pixels_to_categories = mnist.get_training_data()
generator = DigitGenerator()
generator.train(pixels_to_categories=pixels_to_categories)
context.generator = generator
import sys
import os
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from digit_drawing import DigitGenerator
import helpers
from datasets import mnist
dest_folder = 'generated_digits'
image_width = 28
image_height = 28
nepochs = 5
gen = DigitGenerator()
mnist.download_dataset()
pixels_to_categories = mnist.get_training_data()
gen.train(pixels_to_categories=pixels_to_categories, nepochs=nepochs)
print('Training for {} epochs is complete'.format(nepochs))
for i in range(10):
pixels = gen.generate_digit(i)
helpers.create_image(dest_fname=os.path.join(dest_folder,
'digit_{}.png'.format(i)),
pixel_vector=pixels,
width=image_width,
height=image_height)
print('Generated image of a digit {}'.format(i))
def test_neural_net_has_correct_layer_sizes(self):
gen = DigitGenerator()
sizes = gen.net_layer_sizes()
self.assertEqual(len(sizes), 3)
self.assertEqual(sizes[0], 10)
self.assertEqual(sizes[-1], 784)
def test_method_outputs_numpy_array_of_integers(self):
diggen = DigitGenerator()
pixels = diggen.generate_digit(digit=5)
self.assertIsInstance(pixels, np.ndarray)
self.assertEqual(pixels.dtype, np.uint8)
def test_untrained_returns_uniform_array(self):
diggen = DigitGenerator()
pixels = diggen.generate_digit(digit=1)
expected_pixels = np.zeros(784, dtype=np.uint8)
expected_pixels.fill(127)
self.assertEqual(pixels.tolist(), expected_pixels.tolist())
def test_argument_must_be_integer(self):
diggen = DigitGenerator()
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=2.5))
self.assertRaises(DigitGenerator.InvalidDigitError,
lambda: diggen.generate_digit(digit=[4]))
def test_works_for_valid_digits(self):
diggen = DigitGenerator()
for digit in range(10):
diggen.generate_digit(digit=digit)
def test_returned_array_has_valid_shape(self):
diggen = DigitGenerator()
pixels = diggen.generate_digit(digit=1)
self.assertTupleEqual(pixels.shape, (784, ))