tf.strings.unicode_decode

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

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').to_list()
[[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]