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
.
Conditionally creates batches of tensors based on keep_input
. (deprecated)
See docstring in batch
for more details.
Args:
tensors
: The list or dictionary of tensors to enqueue.keep_input
: Abool
Tensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluatesTrue
, thentensors
are all added to the queue. If it is a vector andenqueue_many
isTrue
, then each example is added to the queue only if the corresponding value inkeep_input
isTrue
. This tensor essentially acts as a filtering mechanism.batch_size
: The new batch size pulled from the queue.num_threads
: The number of threads enqueuingtensors
. The batching will be nondeterministic ifnum_threads > 1
.capacity
: An integer. The maximum number of elements in the queue.enqueue_many
: Whether each tensor intensors
is a single example.shapes
: (Optional) The shapes for each example. Defaults to the inferred shapes fortensors
.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. IfTrue
, 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 theshapes
are not specified, and cannot be inferred from the elements oftensors
.