Python – tensorflow.math.squared_difference()

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TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.

squared_difference() is used to compute element wise (x-y)(x-y).

Syntax: tensorflow.math.squared_difference(x, y, name)

Parameters:

x: It’s a tensor. Allowed dtypes are bfloat16, half, float32, float64, complex64, complex128.

y: It’s a tensor of same dtype as x.

name(optional): It defines the name for the operation.

Returns: It returns a tensor.

Example 1:

Python3

# importing the library 
import tensorflow as tf 
  
# Initializing the input tensor 
a = tf.constant([ -5, -7, 2, 5, 7], dtype = tf.float64) 
b = tf.constant([ 1, 3, 9, 4, 7], dtype = tf.float64) 
  
# Printing the input tensor 
print('a: ', a) 
print('b: ', b) 
  
# Calculating result 
res = tf.math.squared_difference(a, b) 
  
# Printing the result 
print('Result: ', res) 

Output:

a:  tf.Tensor([-5. -7.  2.  5.  7.], shape=(5, ), dtype=float64)
b:  tf.Tensor([1. 3. 9. 4. 7.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([ 36. 100.  49.   1.   0.], shape=(5, ), dtype=float64)

Example 2: Taking complex input

Python3

# importing the library 
import tensorflow as tf 
  
# Initializing the input tensor 
a = tf.constant([ -5 + 3j, -7-2j, 2 + 1j, 5-7j, 7 + 3j], dtype = tf.complex128) 
b = tf.constant([ 1 + 5j, 3 + 1j, 9-5j, 4 + 3j, 7-6j], dtype = tf.complex128) 
  
# Printing the input tensor 
print('a: ', a) 
print('b: ', b) 
  
# Calculating result 
res = tf.math.squared_difference(a, b) 
  
# Printing the result 
print('Result: ', res) 

Output:

a:  tf.Tensor([-5.+3.j -7.-2.j  2.+1.j  5.-7.j  7.+3.j], shape=(5, ), dtype=complex128)
b:  tf.Tensor([1.+5.j 3.+1.j 9.-5.j 4.+3.j 7.-6.j], shape=(5, ), dtype=complex128)
Result:  tf.Tensor([ 40.+0.j 109.+0.j  85.+0.j 101.+0.j  81.+0.j], shape=(5, ), dtype=complex128)