def test_downloading_will_create_necessary_files(self):
if not os.environ.get('run_slow', None):
return
download_dataset()
self.assertTrue(self.file_exists('train-images-idx3-ubyte.gz',
9912422))
self.assertTrue(self.file_exists('train-labels-idx1-ubyte.gz', 28881))
self.assertTrue(self.file_exists('t10k-images-idx3-ubyte.gz', 1648877))
self.assertTrue(self.file_exists('t10k-labels-idx1-ubyte.gz', 4542))
def test_get_training_data(self):
if not os.environ.get('run_slow', None):
return
download_dataset()
X_train, Y_train = get_training_data()
self.assertEqual(len(X_train), 60000)
self.assertEqual(len(Y_train), 60000)
self.assertTupleEqual(X_train[0].shape, (28 * 28, ))
self.assertTupleEqual(Y_train[0].shape, (10, ))
self.assertEqual(X_train[0].dtype, 'float64')
self.assertEqual(Y_train[0].dtype, 'float64')
def test_get_test_data(self):
if not os.environ.get('run_slow', None):
return
download_dataset()
X, Y = get_test_data()
self.assertEqual(len(X), 10000)
self.assertEqual(len(Y), 10000)
self.assertTupleEqual(X[0].shape, (28 * 28, ))
self.assertTupleEqual(Y[0].shape, (10, ))
self.assertEqual(X[0].dtype, 'float64')
self.assertEqual(Y[0].dtype, 'float64')
def _load_mnist_examples(self):
mnist.download_dataset()
train_data = mnist.get_training_data()
test_data = mnist.get_test_data()
dataset_size = self._config['dataset_size']
if dataset_size:
train_size = dataset_size
test_size = dataset_size
else:
train_size = len(train_data)
test_size = len(test_data)
self._data_src = PreloadSource(train_data[:train_size])
self._test_data_src = PreloadSource(test_data[:test_size])
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
def step(context):
mnist.download_dataset()
context.training_data = PreloadSource(mnist.get_training_data())
context.test_data = PreloadSource(mnist.get_test_data())
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))