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
See docstring in
batch for more details.
tensors: The list or dictionary of tensors to enqueue.
boolscalar Tensor. This tensor controls whether the input is added to the queue or not. If it evaluates
tensorsare 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
capacity: An integer. The maximum number of elements in the queue.
enqueue_many: Whether each tensor in
tensorsis a single example.
shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for
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.
A list or dictionary of tensors with the same types as
ValueError: If the
shapesare not specified, and cannot be inferred from the elements of