tf.strings.regex_full

Split elements of input based on sep into a RaggedTensor.

tf.strings.split(
    input, sep=None, maxsplit=-1, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Introduction to Tensors tf.data: Build TensorFlow input pipelines Ragged tensors	Simple audio recognition: Recognizing keywords Load images

Let N be the size of input (typically N will be the batch size). Split each element of input based on sep and return a RaggedTensor containing the split tokens. Empty tokens are ignored.

Example:

tf.strings.split('hello world').numpy()
 array([b'hello', b'world'], dtype=object)
tf.strings.split(['hello world', 'a b c'])
<tf.RaggedTensor [[b'hello', b'world'], [b'a', b'b', b'c']]>

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings. For example, input of "1<>2<><>3" and sep of "<>" returns ["1", "2", "", "3"]. If sep is None or an empty string, consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace.

Note that the above mentioned behavior matches python's str.split.

Args
`input`	A string `Tensor` of rank `N`, the strings to split. If `rank(input)` is not known statically, then it is assumed to be `1`.
`sep`	`0-D` string `Tensor`, the delimiter string.
`maxsplit`	An `int`. If `maxsplit > 0`, limit of the split of the result.
`name`	A name for the operation (optional).

Raises
`ValueError`	If sep is not a string.

Returns
A `RaggedTensor` of rank `N+1`, the strings split according to the delimiter.

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.strings / regex_full_match

Used in the notebooks

Example:

Args

Raises

Returns