tf.compat.v1.data.experimental

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"

Args
`datasets`	A list of `tf.data.Dataset` objects with compatible structure.
`choice_dataset`	A `tf.data.Dataset` of scalar `tf.int64` tensors between `0` and `len(datasets) - 1`.

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.

TensorFlow

tf.compat / v1 / v1.data.experimental.Overview

Args

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.data.experimental.Overview

Args

Returns

Raises