tf.train.maybe_shuffle_batch_join

View source on GitHub

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

tf.train.maybe_shuffle_batch_join(
    tensors_list, batch_size, capacity, min_after_dequeue, keep_input, seed=None,
    enqueue_many=False, shapes=None, allow_smaller_final_batch=False,
    shared_name=None, name=None
)

See docstring in shuffle_batch_join for more details.

tensors_list A list of tuples or dictionaries of tensors to enqueue.
batch_size An integer. 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.
seed Seed for the random shuffling within the queue.
enqueue_many Whether each tensor in tensor_list_list is a single example.
shapes (Optional) The shapes for each example. Defaults to the inferred shapes for tensors_list[i].
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 number and types as tensors_list[i].

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

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.