tf.train.maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, keep_input, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None)
See the guide: Inputs and Readers > Input pipeline
Creates batches by randomly shuffling conditionally-enqueued tensors.
See docstring in
shuffle_batch for more details.
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
capacity: An integer. The maximum number of elements in the queue.
min_after_dequeue: Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements.
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.
num_threads: The number of threads enqueuing
seed: Seed for the random shuffling within the queue.
enqueue_many: Whether each tensor in
tensor_listis a single example.
shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for
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 types as
ValueError: If the
shapesare not specified, and cannot be inferred from the elements of