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)
