def inputs(eval_data, input_file):
"""Construct input for evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
input_file: input file with data
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir = FLAGS.data_dir
images, labels = fer2013_input.inputs(eval_data=eval_data,
data_dir=data_dir,
batch_size=FLAGS.batch_size,
input_file=input_file)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
def inputs(eval_data):
"""Construct input for FER2013 evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 1] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_input_dir:
raise ValueError('Please supply a data_input_dir')
data_input_dir = os.path.join(FLAGS.data_input_dir, 'fer2013-batches-bin')
return fer2013_input.inputs(eval_data=eval_data, data_dir=data_input_dir,
batch_size=FLAGS.batch_input_size)
def inputs(eval_data):
"""Construct input for FER2013 evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 1] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir
"""
if not FLAGS.data_input_dir:
raise ValueError('Please supply a data_input_dir')
data_input_dir = os.path.join(FLAGS.data_input_dir, 'fer2013-batches-bin')
return fer2013_input.inputs(eval_data=eval_data, data_dir=data_input_dir,
batch_size=FLAGS.batch_input_size)