tf.data.experimental.choose_from

Creates a Dataset that counts from start in steps of size step.

tf.data.experimental.Counter(
    start=0, step=1, dtype=tf.dtypes.int64
)

Used in the notebooks

Used in the guide	Used in the tutorials
tf.data: Build TensorFlow input pipelines	Data augmentation

For example:

Dataset.count() == [0, 1, 2, ...)
Dataset.count(2) == [2, 3, ...)
Dataset.count(2, 5) == [2, 7, 12, ...)
Dataset.count(0, -1) == [0, -1, -2, ...)
Dataset.count(10, -1) == [10, 9, ...)

Args
`start`	(Optional.) The starting value for the counter. Defaults to 0.
`step`	(Optional.) The step size for the counter. Defaults to 1.
`dtype`	(Optional.) The data type for counter elements. Defaults to `tf.int64`.

Returns
A `Dataset` of scalar `dtype` elements.

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.data / experimental / experimental.choose_from_datasets

Used in the notebooks

For example:

Args

Returns