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)