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)