def inputs():
filenames = [os.path.join(FLAGS.test_dir, FLAGS.test_file)]
for f in filenames:
if not gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
filename_queue = tf.train.string_input_producer(filenames, shuffle=False)
read_input = svhn_input.read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = FLAGS.image_size
width = FLAGS.image_size
float_image = tf.image.per_image_whitening(reshaped_image)
num_preprocess_threads = 1
images, label_batch = tf.train.batch([float_image, read_input.label],
batch_size=FLAGS.batch_size,
num_threads=num_preprocess_threads,
capacity=FLAGS.batch_size)
tf.image_summary('images', images, max_images=29)
return images, tf.reshape(label_batch, [FLAGS.batch_size])
def inputs():
filenames = [os.path.join(FLAGS.test_dir, FLAGS.test_file)]
for f in filenames:
if not gfile.Exists(f):
raise ValueError("Failed to find file: " + f)
filename_queue = tf.train.string_input_producer(filenames, shuffle=False)
read_input = svhn_input.read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = FLAGS.image_size
width = FLAGS.image_size
float_image = tf.image.per_image_whitening(reshaped_image)
num_preprocess_threads = 1
images, label_batch = tf.train.batch(
[float_image, read_input.label],
batch_size=FLAGS.batch_size,
num_threads=num_preprocess_threads,
capacity=FLAGS.batch_size,
)
tf.image_summary("images", images, max_images=29)
return images, tf.reshape(label_batch, [FLAGS.batch_size])
def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if no data_dir
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
# filenames = [os.path.join(FLAGS.data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 7)]
filenames = [os.path.join(FLAGS.data_dir, 'train_batch.bin')]
for f in filenames:
if not gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
# Read examples from files in the filename queue.
read_input = svhn_input.read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
distorted_image = tf.image.random_brightness(reshaped_image, max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2,
upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_whitening(distorted_image)
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
min_fraction_of_examples_in_queue)
print('Filling queue with %d SVHN images before starting to train. '
'This will take a few minutes.' % min_queue_examples)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples)
def distorted_inputs():
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if no data_dir
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
# filenames = [os.path.join(FLAGS.data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 7)]
filenames = [os.path.join(FLAGS.data_dir,'train_batch.bin')]
for f in filenames:
if not gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
# Read examples from files in the filename queue.
read_input = svhn_input.read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
distorted_image = tf.image.random_brightness(reshaped_image,
max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2, upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_whitening(distorted_image)
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
min_fraction_of_examples_in_queue)
print ('Filling queue with %d SVHN images before starting to train. '
'This will take a few minutes.' % min_queue_examples)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples)
