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tf.strings.unicode_encode

TensorFlow 1 version View source on GitHub

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

tf.strings.unicode_encode(
    input,
    output_encoding,
    errors='replace',
    replacement_char=65533,
    name=None
)

Used in the tutorials:

result[i1...iN] is the string formed by concatenating the Unicode codepoints input[1...iN, :], encoded using output_encoding.

Args:

  • input: An N+1 dimensional potentially ragged integer tensor with shape [D1...DN, num_chars].
  • output_encoding: Unicode encoding that should be used to encode each codepoint sequence. Can be "UTF-8", "UTF-16-BE", or "UTF-32-BE".
  • errors: Specifies the response when an invalid codepoint is encountered (optional). One of: * 'replace': Replace invalid codepoint with the replacement_char. (default) * 'ignore': Skip invalid codepoints. * 'strict': Raise an exception for any invalid codepoint.
  • replacement_char: The replacement character codepoint to be used in place of any invalid input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character which is 0xFFFD (U+65533).
  • name: A name for the operation (optional).

Returns:

A N dimensional string tensor with shape [D1...DN].

Example:

input = tf.ragged.constant( 
    [[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]]) 
print(unicode_encode(input, 'UTF-8')) 
tf.Tensor([b'G\xc3\xb6\xc3\xb6dnight' b'\xf0\x9f\x98\x8a'], 
          shape=(2,), dtype=string) 

Compat aliases