Python – Tensorflow bitwise.bitwise_or() method
Last Updated :
04 Jun, 2020
Tensorflow bitwise.bitwise_or()
method performs the bitwise_or operation and return those bits set, that are either set(1) in a or in b or in both. The operation is done on the representation of a and b.
This method belongs to bitwise module.
Syntax: tf.bitwise.bitwise_or( 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_or(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_22:0", shape=(), dtype=int32)
43
Input 2 Tensor("Const_23:0", shape=(), dtype=int32)
5
Output: Tensor("BitwiseOr_1:0", shape=(), dtype=int32)
47
Example 2:
import tensorflow as tf
a = tf.constant([ 1 , 6 ], dtype = tf.int32)
b = tf.constant([ 2 , 5 ], dtype = tf.int32)
c = tf.bitwise.bitwise_or(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_20:0", shape=(2, ), dtype=int32)
[1 6]
Input 2 Tensor("Const_21:0", shape=(2, ), dtype=int32)
[2 5]
Output: Tensor("BitwiseOr:0", shape=(2, ), dtype=int32)
[3 7]
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