tf.strings.unicode_decode

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Decodes each string in input into a sequence of Unicode code points.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.strings.unicode_decode, `tf.compat.v2.strings.unicode_decode`

tf.strings.unicode_decode(
    input, input_encoding, errors='replace', replacement_char=65533,
    replace_control_characters=False, name=None
)

result[i1...iN, j] is the Unicode codepoint for the jth character in input[i1...iN], when decoded using input_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: 'strict': Raise an exception for any illegal substrings. 'replace': Replace illegal substrings with replacement_char. 'ignore': Skip illegal substrings.
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 N+1 dimensional int32 tensor with shape [D1...DN, (num_chars)]. The returned tensor is a tf.Tensor if input is a scalar, or a tf.RaggedTensor otherwise.

Example:

>>> input = [s.encode('utf8') for s in (u'G\xf6\xf6dnight', u'\U0001f60a')]
>>> tf.strings.unicode_decode(input, 'UTF-8').tolist()
[[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]