def convert_split_images(images, labels, train_validation_split=10):
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if labels and images count doesn't match.
Args:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
train_validation_split:
Returns:
"""
# TODO complete doc
assert images.shape[0] == labels.shape[0], "Number of images, %d should be equal to number of labels %d" % \
(images.shape[0], labels.shape[0])
validation_size = images.shape[
0] * train_validation_split // 100 # default 10%
# Generate a validation set.
validation_images = images[:validation_size, :, :, :]
validation_labels = labels[:validation_size]
train_images = images[validation_size:, :, :, :]
train_labels = labels[validation_size:]
# Convert to Examples and write the result to TFRecords.
convert_to_records.convert_to(train_images, train_labels, 'train')
convert_to_records.convert_to(validation_images, validation_labels,
'validation')
def convert_split_images(images, labels, train_validation_split=10):
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if labels and images count doesn't match.
Args:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
train_validation_split:
Returns:
"""
# TODO complete doc
assert images.shape[0] == labels.shape[0], "Number of images, %d should be equal to number of labels %d" % (
images.shape[0],
labels.shape[0],
)
validation_size = images.shape[0] * train_validation_split // 100 # default 10%
# Generate a validation set.
validation_images = images[:validation_size, :, :, :]
validation_labels = labels[:validation_size]
train_images = images[validation_size:, :, :, :]
train_labels = labels[validation_size:]
# Convert to Examples and write the result to TFRecords.
convert_to_records.convert_to(train_images, train_labels, "train")
convert_to_records.convert_to(validation_images, validation_labels, "validation")
def convert_images(images, labels, filename):
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if labels and images count doesn't match.
Args:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
filename:
Returns:
"""
# TODO complete doc
assert images.shape[0] == labels.shape[0], "Number of images, %d should be equal to number of labels %d" % \
(images.shape[0], labels.shape[0])
# Convert to Examples and write the result to TFRecords.
convert_to_records.convert_to(images, labels, filename)
def convert_images(images, labels, filename):
"""Construct distorted input for CIFAR training using the Reader ops.
Raises:
ValueError: if labels and images count doesn't match.
Args:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
filename:
Returns:
"""
# TODO complete doc
assert images.shape[0] == labels.shape[0], "Number of images, %d should be equal to number of labels %d" % (
images.shape[0],
labels.shape[0],
)
# Convert to Examples and write the result to TFRecords.
convert_to_records.convert_to(images, labels, filename)
