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.
boolTensor. This tensor controls whether the input is added to the queue or not. If it is a scalar and evaluates
tensorsare all added to the queue. If it is a vector and
True, then each example is added to the queue only if the corresponding value in
True. 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
Input pipelines based on Queues are not supported when eager execution is
enabled. Please use the
tf.data API to ingest data under eager execution.