Elementwise computes the bitwise XOR of x
and y
.
tf.bitwise.bitwise_xor(
x, y, name=None
)
The result will have those bits set, that are different in x
and y
. The
computation is performed on the underlying representations of x
and y
.
For example:
import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64,
tf.uint8, tf.uint16, tf.uint32, tf.uint64]
for dtype in dtype_list:
lhs = tf.constant([0, 5, 3, 14], dtype=dtype)
rhs = tf.constant([5, 0, 7, 11], dtype=dtype)
exp = tf.constant([5, 5, 4, 5], dtype=tf.float32)
res = bitwise_ops.bitwise_xor(lhs, rhs)
tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: int8 , int16 , int32 , int64 , uint8 , uint16 , uint32 , uint64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|