message: Fix batch_size in cifar10_main
diff: @@ -82,7 +82,7 @@ _BATCHES_PER_EPOCH = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
  def record_dataset(filenames):
  """Returns an input pipeline Dataset from `filenames`."""
  record_bytes = HEIGHT * WIDTH * DEPTH + 1
  - return tf.data.FixedLengthRecordDataset(filenames, record_bytes).prefetch(2*batch_size)
  + return tf.data.FixedLengthRecordDataset(filenames, record_bytes).prefetch(2*FLAGS.batch_size)
  def filenames(mode):
  @@ -157,12 +157,10 @@ def input_fn(mode, batch_size):
  dataset = dataset.repeat()
  dataset = dataset.map(dataset_parser, num_parallel_calls=1)
  -# output_buffer_size=2 * batch_size)
  # For training, preprocess the image and shuffle.
  if mode == tf.estimator.ModeKeys.TRAIN:
  dataset = dataset.map(train_preprocess_fn, num_parallel_calls=1)
  -# output_buffer_size=2 * batch_size)
  # Ensure that the capacity is sufficiently large to provide good random
  # shuffling.
  @@ -173,7 +171,6 @@ def input_fn(mode, batch_size):
  dataset = dataset.map(
  lambda image, label: (tf.image.per_image_standardization(image), label),
  num_parallel_calls=1)
  -# output_buffer_size=2 * batch_size)
  # Batch results by up to batch_size, and then fetch the tuple from the
  # iterator.
  