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).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 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/v2.13.1/tensorflow/python/data/ops/dataset_ops.py#L4242-L4272) |\n\nCreates an iterator for elements of `dataset`. \n\n tf.compat.v1.data.make_one_shot_iterator(\n dataset\n )\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| **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