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.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.train.maybe_shuffle_batch\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/training/input.py#L1350-L1411) |\n\nCreates batches by randomly shuffling conditionally-enqueued tensors. (deprecated)\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.train.maybe_shuffle_batch`](/api_docs/python/tf/compat/v1/train/maybe_shuffle_batch)\n\n\u003cbr /\u003e\n\n tf.train.maybe_shuffle_batch(\n tensors, batch_size, capacity, min_after_dequeue, keep_input, num_threads=1,\n seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False,\n shared_name=None, name=None\n )\n\n| **Warning:** 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.filter(...).shuffle(min_after_dequeue).batch(batch_size)`.\n\nSee docstring in `shuffle_batch` for more details.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `tensors` | The list or dictionary of tensors to enqueue. |\n| `batch_size` | 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| `num_threads` | The number of threads enqueuing `tensor_list`. |\n| `seed` | Seed for the random shuffling within the queue. |\n| `enqueue_many` | Whether each tensor in `tensor_list` is a single example. |\n| `shapes` | (Optional) The shapes for each example. Defaults to the inferred shapes for `tensor_list`. |\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 types as `tensors`. ||\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`. |\n\n\u003cbr /\u003e\n\n#### Eager Compatibility\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."]]
View source on GitHub