tf.train.maybe_batch

tf.train.maybe_batch(
    tensors,
    keep_input,
    batch_size,
    num_threads=1,
    capacity=32,
    enqueue_many=False,
    shapes=None,
    dynamic_pad=False,
    allow_smaller_final_batch=False,
    shared_name=None,
    name=None
)

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

See the guide: Inputs and Readers > Input pipeline

Conditionally creates batches of tensors based on keep_input.

See docstring in batch for more details.

Args:

  • tensors: The list or dictionary of tensors to enqueue.
  • 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.
  • batch_size: The new batch size pulled from the queue.
  • num_threads: The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
  • capacity: An integer. The maximum number of elements in the queue.
  • enqueue_many: Whether each tensor in tensors is a single example.
  • shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
  • dynamic_pad: Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
  • 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 types as tensors.

Raises:

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