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 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))