# tf.train.maybe_batch

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


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.