tf.train.maybe_shuffle_batch

tf.train.maybe_shuffle_batch(
    tensors,
    batch_size,
    capacity,
    min_after_dequeue,
    keep_input,
    num_threads=1,
    seed=None,
    enqueue_many=False,
    shapes=None,
    allow_smaller_final_batch=False,
    shared_name=None,
    name=None
)

Defined in tensorflow/python/training/input.py.

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

See docstring in shuffle_batch for more details.

Args:

  • tensors: The list or dictionary of tensors to enqueue.
  • batch_size: 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.
  • num_threads: The number of threads enqueuing tensor_list.
  • seed: Seed for the random shuffling within the queue.
  • enqueue_many: Whether each tensor in tensor_list is a single example.
  • shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
  • 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 types as tensors.

Raises:

  • ValueError: If the shapes are not specified, and cannot be inferred from the elements of tensors.

Eager Compatibility

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.