TensorFlow 2 version | View source on GitHub |
Decodes each string into a sequence of code points with start offsets.
tf.strings.unicode_decode_with_offsets(
input, input_encoding, errors='replace', replacement_char=65533,
replace_control_characters=False, name=None
)
This op is similar to tf.strings.decode(...)
, but it also returns the
start offset for each character in its respective string. This information
can be used to align the characters with the original byte sequence.
Returns a tuple (codepoints, start_offsets)
where:
codepoints[i1...iN, j]
is the Unicode codepoint for thej
th character ininput[i1...iN]
, when decoded usinginput_encoding
.start_offsets[i1...iN, j]
is the start byte offset for thej
th character ininput[i1...iN]
, when decoded usinginput_encoding
.
Args | |
---|---|
input
|
An N dimensional potentially ragged string tensor with shape
[D1...DN] . N must be statically known.
|
input_encoding
|
String name for the unicode encoding that should be used to decode each string. |
errors
|
Specifies the response when an input string can't be converted
using the indicated encoding. One of:
|
replacement_char
|
The replacement codepoint to be used in place of invalid
substrings in input when errors='replace' ; and in place of C0 control
characters in input when replace_control_characters=True .
|
replace_control_characters
|
Whether to replace the C0 control characters
(U+0000 - U+001F) with the replacement_char .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A tuple of N+1 dimensional tensors (codepoints, start_offsets) .
The returned tensors are |
Example:
>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')] >>> result = tf.strings.unicode_decode_with_offsets(input, 'UTF-8') >>> result[0].tolist() # codepoints [[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]] >>> result[1].tolist() # offsets [[0, 1, 3, 5, 6, 7, 8, 9, 10], [0]]