def testMultiTrain(self):
data_dir = MNIST_DATA_DIR + MNIST_MULTI_TRAIN_FILE
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(data_dir=data_dir,
batch_size=1,
split='train',
num_targets=2,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, labels, recons_image, spare_image = sess.run([
features['images'], features['labels'],
features['recons_image'], features['spare_image']
])
self.assertEqual((1, 10), labels.shape)
self.assertEqual(2, np.sum(labels))
self.assertItemsEqual([0, 1], np.unique(labels))
self.assertEqual(36, features['height'])
self.assertEqual((1, 36 * 36), images.shape)
self.assertEqual(recons_image.shape, images.shape)
self.assertEqual(spare_image.shape, images.shape)
coord.request_stop()
for thread in threads:
thread.join()
def testSingleTrainDistorted(self):
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(data_dir=MNIST_DATA_DIR,
batch_size=1,
split='train',
num_targets=1,
distort=True,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, labels, recons_image = sess.run([
features['images'], features['labels'],
features['recons_image']
])
self.assertEqual((1, 10), labels.shape)
self.assertEqual(1, np.sum(labels))
self.assertItemsEqual([0, 1], np.unique(labels))
self.assertEqual(24, features['height'])
self.assertEqual((1, 24, 24, 1), images.shape)
self.assertEqual(recons_image.shape, images.shape)
self.assertAllEqual(recons_image, images)
coord.request_stop()
for thread in threads:
thread.join()
def testMultiTrain(self):
data_dir = MNIST_DATA_DIR + MNIST_MULTI_TRAIN_FILE
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(
data_dir=data_dir,
batch_size=1,
split='train',
num_targets=2,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, labels, recons_image, spare_image = sess.run([
features['images'], features['labels'], features['recons_image'],
features['spare_image']
])
self.assertEqual((1, 10), labels.shape)
self.assertEqual(2, np.sum(labels))
self.assertItemsEqual([0, 1], np.unique(labels))
self.assertEqual(36, features['height'])
self.assertEqual((1, 36 * 36), images.shape)
self.assertEqual(recons_image.shape, images.shape)
self.assertEqual(spare_image.shape, images.shape)
coord.request_stop()
for thread in threads:
thread.join()
def testMultiTest(self):
data_dir = MNIST_DATA_DIR + MNIST_MULTI_TEST_FILE
with self.test_session(graph=tf.Graph()) as sess:
test_features = mnist_input_record.inputs(data_dir=data_dir,
batch_size=1,
split='test',
num_targets=2,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
test_label = sess.run([test_features['recons_label']])
self.assertEqual([7], test_label)
coord.request_stop()
for thread in threads:
thread.join()
def testMultiTest(self):
data_dir = MNIST_DATA_DIR + MNIST_MULTI_TEST_FILE
with self.test_session(graph=tf.Graph()) as sess:
test_features = mnist_input_record.inputs(
data_dir=data_dir,
batch_size=1,
split='test',
num_targets=2,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
test_label = sess.run([test_features['recons_label']])
self.assertEqual([7], test_label)
coord.request_stop()
for thread in threads:
thread.join()
def testSingleTestDistorted(self):
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(
data_dir=MNIST_DATA_DIR,
batch_size=1,
split='test',
num_targets=1,
distort=True,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, recons_image, recons_label = sess.run([
features['images'], features['recons_image'], features['recons_label']
])
self.assertEqual([7], recons_label)
self.assertEqual(24, features['height'])
self.assertEqual((1, 24, 24, 1), images.shape)
self.assertAllEqual(recons_image, images)
coord.request_stop()
for thread in threads:
thread.join()
def testSingleTestDistorted(self):
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(data_dir=MNIST_DATA_DIR,
batch_size=1,
split='test',
num_targets=1,
distort=True,
evaluate=True,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, recons_image, recons_label = sess.run([
features['images'], features['recons_image'],
features['recons_label']
])
self.assertEqual([7], recons_label)
self.assertEqual(24, features['height'])
self.assertEqual((1, 24, 24, 1), images.shape)
self.assertAllEqual(recons_image, images)
coord.request_stop()
for thread in threads:
thread.join()
def testSingleTrain(self):
with self.test_session(graph=tf.Graph()) as sess:
features = mnist_input_record.inputs(
data_dir=MNIST_DATA_DIR,
batch_size=1,
split='train',
num_targets=1,
batch_capacity=2)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
images, labels, recons_image = sess.run(
[features['images'], features['labels'], features['recons_image']])
self.assertEqual((1, 10), labels.shape)
self.assertEqual(1, np.sum(labels))
self.assertItemsEqual([0, 1], np.unique(labels))
self.assertEqual(28, features['height'])
self.assertEqual((1, 28, 28, 1), images.shape)
self.assertEqual(recons_image.shape, images.shape)
self.assertAllEqual(recons_image, images)
coord.request_stop()
for thread in threads:
thread.join()