def test_dataset_input_fn(self):
fake_data = bytearray()
fake_data.append(7)
for i in range(_NUM_CHANNELS):
for _ in range(_HEIGHT * _WIDTH):
fake_data.append(i)
_, filename = mkstemp(dir=self.get_temp_dir())
data_file = open(filename, 'wb')
data_file.write(fake_data)
data_file.close()
fake_dataset = tf.data.FixedLengthRecordDataset(
filename, cifar10_main._RECORD_BYTES)
fake_dataset = fake_dataset.map(
lambda val: cifar10_main.parse_record(val, False))
image, label = fake_dataset.make_one_shot_iterator().get_next()
self.assertAllEqual(label.shape, (10,))
self.assertAllEqual(image.shape, (_HEIGHT, _WIDTH, _NUM_CHANNELS))
with self.test_session() as sess:
image, label = sess.run([image, label])
self.assertAllEqual(label, np.array([int(i == 7) for i in range(10)]))
for row in image:
for pixel in row:
self.assertAllClose(pixel, np.array([-1.225, 0., 1.225]), rtol=1e-3)
def test_dataset_input_fn(self):
fake_data = bytearray()
fake_data.append(7)
for i in range(_NUM_CHANNELS):
for _ in range(_HEIGHT * _WIDTH):
fake_data.append(i)
_, filename = mkstemp(dir=self.get_temp_dir())
data_file = open(filename, 'wb')
data_file.write(fake_data)
data_file.close()
fake_dataset = tf.data.FixedLengthRecordDataset(
filename, cifar10_main._RECORD_BYTES)
fake_dataset = fake_dataset.map(
lambda val: cifar10_main.parse_record(val, False))
image, label = fake_dataset.make_one_shot_iterator().get_next()
self.assertAllEqual(label.shape, (10, ))
self.assertAllEqual(image.shape, (_HEIGHT, _WIDTH, _NUM_CHANNELS))
with self.test_session() as sess:
image, label = sess.run([image, label])
self.assertAllEqual(label,
np.array([int(i == 7) for i in range(10)]))
for row in image:
for pixel in row:
self.assertAllClose(pixel,
np.array([-1.225, 0., 1.225]),
rtol=1e-3)
def parse_record_keras(raw_record, is_training, dtype):
"""Parses a record containing a training example of an image.
The input record is parsed into a label and image, and the image is passed
through preprocessing steps (cropping, flipping, and so on).
This method converts the label to one hot to fit the loss function.
Args:
raw_record: scalar Tensor tf.string containing a serialized
Example protocol buffer.
is_training: A boolean denoting whether the input is for training.
dtype: Data type to use for input images.
Returns:
Tuple with processed image tensor and one-hot-encoded label tensor.
"""
image, label = cifar_main.parse_record(raw_record, is_training, dtype)
label = tf.sparse_to_dense(label, (cifar_main.NUM_CLASSES, ), 1)
return image, label