tf.train.maybe_shuffle_batch

Creates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.train.maybe_shuffle_batch

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 docstring in shuffle_batch for more details.

Args
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.
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.
num_threads	The number of threads enqueuing tensor_list.
seed	Seed for the random shuffling within the queue.
enqueue_many	Whether each tensor in tensor_list is a single example.
shapes	(Optional) The shapes for each example. Defaults to the inferred shapes for tensor_list.
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 types as tensors.

Raises
ValueError	If the shapes are not specified, and cannot be inferred from the elements of tensors.

Eager Compatibility

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.