# Python – tensorflow.math.squared_difference()

• Last Updated : 16 Jun, 2020

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)
```

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