c1db25d9281a9374005d5c8b4e183b8e6d7d2ace,cloud_tpu/models/resnet/estimator_resnet.py,,input_fn,#Any#Any#,193

Before Change

      num_threads=FLAGS.map_threads,
      output_buffer_size=FLAGS.map_buffer_size or batch_size)
  dataset = dataset.batch(batch_size)
  dataset = dataset.prefetch(2)
  images, labels = dataset.make_one_shot_iterator().get_next()
  // TODO(xiejw,saeta): Consider removing the sharding dimension below.
  images_shape = images.get_shape().as_list()
  if images_shape[0] is None:

After Change

  dataset = tf.contrib.data.Dataset.list_files(input_dataset.file_pattern)
  if is_training:
    if FLAGS.input_files_shuffle_capacity > 0:
      dataset = dataset.shuffle(FLAGS.input_files_shuffle_capacity)
    dataset = dataset.repeat()

  def prefetch_dataset(filename):
    dataset = tf.contrib.data.TFRecordDataset(filename)

Instances