tf.compat.v1.train.maybe_batch

Conditionally creates batches of tensors based on keep_input. (deprecated)

tf.compat.v1.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
)

See docstring in batch for more details.

tensorsThe list or dictionary of tensors to enqueue.
keep_inputA 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_sizeThe new batch size pulled from the queue.
num_threadsThe number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
capacityAn integer. The maximum number of elements in the queue.
enqueue_manyWhether each tensor in tensors is a single example.
shapes(Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
dynamic_padBoolean. 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.

A list or dictionary of tensors with the same types as tensors.

ValueErrorIf the shapes are not specified, and cannot be inferred from the elements of tensors.