This is a legacy API for consuming dataset elements and should only be used
during transition from TF 1 to TF 2. Note that using this API should be
a transient state of your code base as there are in general no guarantees
about the interoperability of TF 1 and TF 2 code.
In TF 2 datasets are Python iterables which means you can consume their
elements using for elem in dataset: ... or by explicitly creating iterator
via iterator = iter(dataset) and fetching its elements via
values = next(iterator).
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.compat.v1.data.make_one_shot_iterator\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/data/ops/dataset_ops.py#L4256-L4288) |\n\nCreates an iterator for elements of `dataset`. \n\n tf.compat.v1.data.make_one_shot_iterator(\n dataset: ../../../../tf/compat/v1/data/Dataset\n ) -\u003e Union[iterator_ops.Iterator, iterator_ops.OwnedIterator]\n\n\u003cbr /\u003e\n\nMigrate to TF2\n--------------\n\n\u003cbr /\u003e\n\n| **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.\n\nThis is a legacy API for consuming dataset elements and should only be used\nduring transition from TF 1 to TF 2. Note that using this API should be\na transient state of your code base as there are in general no guarantees\nabout the interoperability of TF 1 and TF 2 code.\n\nIn TF 2 datasets are Python iterables which means you can consume their\nelements using `for elem in dataset: ...` or by explicitly creating iterator\nvia `iterator = iter(dataset)` and fetching its elements via\n`values = next(iterator)`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nDescription\n-----------\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Exploring the TF-Hub CORD-19 Swivel Embeddings](https://www.tensorflow.org/hub/tutorials/cord_19_embeddings) - [Graph-based Neural Structured Learning in TFX](https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning) - [Preprocessing data with TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/census) |\n\n| **Note:** The returned iterator will be initialized automatically. A \"one-shot\" iterator does not support re-initialization.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|-----------------------------------------------------|\n| `dataset` | A [`tf.data.Dataset`](../../../../tf/data/Dataset). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A [`tf.data.Iterator`](../../../../tf/data/Iterator) for elements of `dataset`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub