Function maybe_shuffle_batch_join

tensorflow/python/training/input.py:1517–1573

Create batches by randomly shuffling conditionally-enqueued tensors. See docstring in `shuffle_batch_join` for more details. Args: tensors_list: A list of tuples or dictionaries of tensors to enqueue. batch_size: An integer. The new batch size pulled from the queue. capacity: An in

(tensors_list, batch_size, capacity,
                             min_after_dequeue, keep_input, seed=None,
                             enqueue_many=False, shapes=None,
                             allow_smaller_final_batch=False, shared_name=None,
                             name=None)

    "`tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue)"
    ".batch(batch_size)`.")
def maybe_shuffle_batch_join(tensors_list, batch_size, capacity,
                             min_after_dequeue, keep_input, seed=None,
                             enqueue_many=False, shapes=None,
                             allow_smaller_final_batch=False, shared_name=None,
                             name=None):
  """Create batches by randomly shuffling conditionally-enqueued tensors.

  See docstring in `shuffle_batch_join` for more details.

  Args:
    tensors_list: A list of tuples or dictionaries of tensors to enqueue.
    batch_size: An integer. The new batch size pulled from the queue.
    capacity: An integer. The maximum number of elements in the queue.
    min_after_dequeue: Minimum number elements in the queue after a
      dequeue, used to ensure a level of mixing of elements.
    keep_input: A `bool` Tensor.  This tensor controls whether the input is
      added to the queue or not.  If it is a scalar and evaluates `True`, then
      `tensors` are all added to the queue. If it is a vector and `enqueue_many`
      is `True`, then each example is added to the queue only if the
      corresponding value in `keep_input` is `True`. This tensor essentially
      acts as a filtering mechanism.
    seed: Seed for the random shuffling within the queue.
    enqueue_many: Whether each tensor in `tensor_list_list` is a single
      example.
    shapes: (Optional) The shapes for each example.  Defaults to the
      inferred shapes for `tensors_list[i]`.
    allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
      batch to be smaller if there are insufficient items left in the queue.
    shared_name: (optional). If set, this queue will be shared under the given
      name across multiple sessions.
    name: (Optional) A name for the operations.

  Returns:
    A list or dictionary of tensors with the same number and types as
    `tensors_list[i]`.

  Raises:
    ValueError: If the `shapes` are not specified, and cannot be
      inferred from the elements of `tensors_list`.

  @compatibility(eager)
  Input pipelines based on Queues are not supported when eager execution is
  enabled. Please use the `tf.data` API to ingest data under eager execution.
  @end_compatibility
  """
  return _shuffle_batch_join(
      tensors_list,
      batch_size,
      capacity,
      min_after_dequeue,
      keep_input,
      seed=seed,
      enqueue_many=enqueue_many,
      shapes=shapes,
      allow_smaller_final_batch=allow_smaller_final_batch,
      shared_name=shared_name,
      name=name)