See docstring in shuffle_batch_join for more details.
Args
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.
Returns
A list or dictionary of tensors with the same number and types as
tensors_list[i].
Raises
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.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.compat.v1.train.maybe_shuffle_batch_join\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/training/input.py#L1516-L1577) |\n\nCreate batches by randomly shuffling conditionally-enqueued tensors. (deprecated) \n\n tf.compat.v1.train.maybe_shuffle_batch_join(\n tensors_list,\n batch_size,\n capacity,\n min_after_dequeue,\n keep_input,\n seed=None,\n enqueue_many=False,\n shapes=None,\n allow_smaller_final_batch=False,\n shared_name=None,\n name=None\n )\n\n| **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by [`tf.data`](../../../../tf/data). Use `tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue).batch(batch_size)`.\n\nSee docstring in `shuffle_batch_join` for more details.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `tensors_list` | A list of tuples or dictionaries of tensors to enqueue. |\n| `batch_size` | An integer. The new batch size pulled from the queue. |\n| `capacity` | An integer. The maximum number of elements in the queue. |\n| `min_after_dequeue` | Minimum number elements in the queue after a dequeue, used to ensure a level of mixing of elements. |\n| `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. |\n| `seed` | Seed for the random shuffling within the queue. |\n| `enqueue_many` | Whether each tensor in `tensor_list_list` is a single example. |\n| `shapes` | (Optional) The shapes for each example. Defaults to the inferred shapes for `tensors_list[i]`. |\n| `allow_smaller_final_batch` | (Optional) Boolean. If `True`, allow the final batch to be smaller if there are insufficient items left in the queue. |\n| `shared_name` | (optional). If set, this queue will be shared under the given name across multiple sessions. |\n| `name` | (Optional) A name for the operations. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A list or dictionary of tensors with the same number and types as `tensors_list[i]`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|------------------------------------------------------------------------------------------------|\n| `ValueError` | If the `shapes` are not specified, and cannot be inferred from the elements of `tensors_list`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\neager compatibility\n-------------------\n\n\u003cbr /\u003e\n\nInput pipelines based on Queues are not supported when eager execution is\nenabled. Please use the [`tf.data`](../../../../tf/data) API to ingest data under eager execution.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e"]]
View source on GitHub