tf.compat.v1.data.experimental.choose_from_datasets
Stay organized with collections
Save and categorize content based on your preferences.
Creates a dataset that deterministically chooses elements from datasets
.
tf.compat.v1.data.experimental.choose_from_datasets(
datasets, choice_dataset
)
For example, given the following datasets:
datasets = [tf.data.Dataset.from_tensors("foo").repeat(),
tf.data.Dataset.from_tensors("bar").repeat(),
tf.data.Dataset.from_tensors("baz").repeat()]
# Define a dataset containing `[0, 1, 2, 0, 1, 2, 0, 1, 2]`.
choice_dataset = tf.data.Dataset.range(3).repeat(3)
result = tf.data.experimental.choose_from_datasets(datasets, choice_dataset)
The elements of result
will be:
"foo", "bar", "baz", "foo", "bar", "baz", "foo", "bar", "baz"
Returns |
A dataset that interleaves elements from datasets according to the values
of choice_dataset .
|
Raises |
TypeError
|
If the datasets or choice_dataset arguments have the wrong
type.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2021-02-18 UTC.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2021-02-18 UTC."],[],[]]