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Python – Tensorflow bitwise.left_shift() method

  • Last Updated : 04 Jun, 2020

Tensorflow bitwise.left_shift() method performs the left_shift operation on input a defined by input b and return the new constant. The operation is done on the representation of a and b.
This method belongs to bitwise module.

Syntax: tf.bitwise.left_shift( 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 constant a and b
a = tf.constant(8, dtype = tf.int32)
b = tf.constant(2, dtype = tf.int32)  
  
# Applying the left_shift() function 
# storing the result in 'c' 
c = tf.bitwise.left_shift(a, b) 
  
# Initiating a Tensorflow session 
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_49:0", shape=(), dtype=int32)
8
Input 2 Tensor("Const_50:0", shape=(), dtype=int32)
2
Output:  Tensor("LeftShift_1:0", shape=(), dtype=int32)
32

Example 2:




import tensorflow as tf 
  
# A constant a and b
a = tf.constant([8, 16, 32], dtype = tf.int32)
b = tf.constant([2, 2, 3], dtype = tf.int32)  
  
# Applying the left_shift() function 
# storing the result in 'c' 
c = tf.bitwise.left_shift(a, b) 
  
# Initiating a Tensorflow session 
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_51:0", shape=(3, ), dtype=int32)
[ 8 16 32]
Input 2 Tensor("Const_52:0", shape=(3, ), dtype=int32)
[2 2 3]
Output:  Tensor("LeftShift_2:0", shape=(3, ), dtype=int32)
[ 32  64 256]




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