def shuffle(tensors,
capacity=32,
min_after_dequeue=16,
num_threads=1,
dtypes=None,
shapes=None,
seed=None,
shared_name=None,
name='shuffle'):
"""Wrapper around a `tf.RandomShuffleQueue` creation.
Return a dequeue op that dequeues elements from `tensors` in a
random order, through a `tf.RandomShuffleQueue` -- see for further
documentation.
Arguments:
tensors: an iterable of tensors.
capacity: (Optional) the capacity of the queue; default value set to 32.
num_threads: (Optional) the number of threads to be used fo the queue runner;
default value set to 1.
min_after_dequeue: (Optional) minimum number of elements to remain in the
queue after a `dequeue` or `dequeu_many` has been performend,
in order to ensure better mixing of elements; default value set to 16.
dtypes: (Optional) list of `DType` objects, one for each tensor in `tensors`;
if not provided, will be inferred from `tensors`.
shapes: (Optional) list of shapes, one for each tensor in `tensors`.
seed: (Optional) seed for random shuffling.
shared_name: (Optional) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name scope for the ops.
Returns:
The tuple of tensors that was randomly dequeued from `tensors`.
"""
tensors = list(tensors)
with tf.name_scope(name, tensors):
dtypes = dtypes or list([t.dtype for t in tensors])
queue = tf.RandomShuffleQueue(
seed=seed,
shared_name=shared_name,
name='random_shuffle_queue',
dtypes=dtypes,
shapes=shapes,
capacity=capacity,
min_after_dequeue=min_after_dequeue)
enqueue = queue.enqueue(tensors)
runner = tf.train.QueueRunner(queue, [enqueue] * num_threads)
tf.train.add_queue_runner(runner)
dequeue = queue.dequeue()
return dequeue
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