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)

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)

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 scalar Tensor. This tensor controls whether the input is added to the queue or not. If it evaluates True, then tensors are added to the queue; otherwise they are dropped. 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.
• 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.

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