Tensorflow bitwise.bitwise_xor() method – Python
Tensorflow bitwise.bitwise_xor()
method performs the bitwise_xor operation and the result will set those bits, that are different in a and b. The operation is done on the representation of a and b. This method belongs to bitwise module.
Syntax: tf.bitwise.bitwise_xor(a, b, name=None)
Arguments
- a: This must be a Tensor.It should be from the one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.
- b: This should also be a Tensor, Type same as a.
- name: This is optional parameter and this is the name of the operation.
Return: It returns a Tensor having the same type as a and b.
Let’s see this concept with the help of few examples:
Example 1:
import tensorflow as tf
a = tf.constant( 43 , dtype = tf.int32)
b = tf.constant( 5 , dtype = tf.int32)
c = tf.bitwise.bitwise_xor(a, b)
with tf.Session() as sess:
print ( "Input 1" , a)
print (sess.run(a))
print ( "Input 2" , b)
print (sess.run(b))
print ( "Output: " , c)
print (sess.run(c))
|
Output:
Input 1 Tensor("Const_36:0", shape=(), dtype=int32)
43
Input 2 Tensor("Const_37:0", shape=(), dtype=int32)
5
Output: Tensor("BitwiseXor_4:0", shape=(), dtype=int32)
46
Example 2:
import tensorflow as tf
a = tf.constant([ 10 , 6 ], dtype = tf.int32)
b = tf.constant([ 12 , 5 ], dtype = tf.int32)
c = tf.bitwise.bitwise_xor(a, b)
with tf.Session() as sess:
print ( "Input 1" , a)
print (sess.run(a))
print ( "Input 2" , b)
print (sess.run(b))
print ( "Output: " , c)
print (sess.run(c))
|
Output:
Input 1 Tensor("Const_34:0", shape=(2, ), dtype=int32)
[10 6]
Input 2 Tensor("Const_35:0", shape=(2, ), dtype=int32)
[12 5]
Output: Tensor("BitwiseXor_3:0", shape=(2, ), dtype=int32)
[6 3]
Last Updated :
26 May, 2020
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