tf.bitwise.invert  |  TensorFlow v2.16.1 (original) (raw)

tf.bitwise.invert

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Invert (flip) each bit of supported types; for example, type uint8 value 01010101 becomes 10101010.

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Compat aliases for migration

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tf.compat.v1.bitwise.invert

tf.bitwise.invert(
    x: Annotated[Any, TV_Invert_T], name=None
) -> Annotated[Any, TV_Invert_T]

Flip each bit of supported types. For example, type int8 (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101. This operation is performed on each element of the tensor argument x.

Example:

import tensorflow as tf
from tensorflow.python.ops import bitwise_ops

# flip 2 (00000010) to -3 (11111101)
tf.assert_equal(-3, bitwise_ops.invert(2))

dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
              dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]

inputs = [0, 5, 3, 14]
for dtype in dtype_list:
  # Because of issues with negative numbers, let's test this indirectly.
  # 1. invert(a) and a = 0
  # 2. invert(a) or a = invert(0)
  input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
  not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
                                      input_tensor, bitwise_ops.invert(input_tensor)),
                                    bitwise_ops.bitwise_or(
                                      input_tensor, bitwise_ops.invert(input_tensor)),
                                    bitwise_ops.invert(
                                      tf.constant(0, dtype=dtype))]

  expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
  tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)

  expected = tf.cast([not_0] * 4, tf.float32)
  tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)

  # For unsigned dtypes let's also check the result directly.
  if dtype.is_unsigned:
    inverted = bitwise_ops.invert(input_tensor)
    expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
    tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))