(input_tensors, capacity, min_after_dequeue, num_threads)
return output_tensors
Shuffles tensors in `input_tensors`, maintaining grouping.
Shuffles tensors in `input_tensors`, maintaining grouping.
Shuffles tensors in input_tensors maintaining grouping .
def _shuffle_inputs(input_tensors, capacity, min_after_dequeue, num_threads):
  """Shuffles tensors in `input_tensors`, maintaining grouping."""
  shuffle_queue = tf.RandomShuffleQueue(
      capacity, min_after_dequeue, dtypes=[t.dtype for t in input_tensors])
  enqueue_op = shuffle_queue.enqueue(input_tensors)
  runner = tf.train.QueueRunner(shuffle_queue, [enqueue_op] * num_threads)
  tf.train.add_queue_runner(runner)

  output_tensors = shuffle_queue.dequeue()

  for i in range(len(input_tensors)):
    output_tensors[i].set_shape(input_tensors[i].shape)

  return output_tensors
def _shuffle_inputs ( input_tensors , capacity , min_after_dequeue , num_threads ) : shuffle_queue = tf . RandomShuffleQueue ( capacity , min_after_dequeue , dtypes = [ t . dtype for t in input_tensors ] ) enqueue_op = shuffle_queue . enqueue ( input_tensors ) runner = tf . train . QueueRunner ( shuffle_queue , [ enqueue_op ] * num_threads ) tf . train . add_queue_runner ( runner ) output_tensors = shuffle_queue . dequeue ( ) for i in range ( len ( input_tensors ) ) : output_tensors [ i ] . set_shape ( input_tensors [ i ] . shape ) return output_tensors