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# Python – tensorflow.math.l2_normalize()

• Last Updated : 09 Jun, 2020

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

l2_normalize() is used to normalize a tensor along axis using L2 norm.

Syntax: tensorflow.math.l2_normalize( x, axis, epsilon, name)

Parameters:

• x: It’s the input tensor.
• axis: It defines the dimension along which tensor will be normalized.
• epsilon: It defines the lower bound value for norm. Default value is 1e-12. It uses sqrt(epsilon) as divisor if norm<sqrt(divisor).
• name: An optional parameter that defines the name for the operation.

Returns:

It returns a tensor of same shape as x.

Example 1:

## Python3

 `# Importing the libraray``import` `tensorflow as tf`` ` `# Initializing the input tensor``a ``=` `tf.constant([``7``, ``8``, ``13``, ``11``], dtype ``=` `tf.float64)`` ` `# Printing the input tensor``print``(``'a: '``, a)`` ` `# Calculating the result``res ``=` `tf.math.l2_normalize(a)`` ` `# Printing the result``print``(``'Result: '``, res)`

Output:

```a:  tf.Tensor([ 7.  8. 13. 11.], shape=(4, ), dtype=float64)
Result:  tf.Tensor([0.34869484 0.39850839 0.64757613 0.54794903], shape=(4, ), dtype=float64)

```

Example 2: This example uses 2-D tensor.

## Python3

 `# Importing the libraray``import` `tensorflow as tf`` ` `# Initializing the input tensor``a ``=` `tf.constant([[``7``, ``8``], [``13``, ``11``]], dtype ``=` `tf.float64)`` ` `# Printing the input tensor``print``(``'a: '``, a)`` ` `# Calculating the result``res ``=` `tf.math.l2_normalize(x ``=` `a, axis ``=` `1``)`` ` `# Printing the result``print``(``'Result: '``, res)`

Output:

```a:  tf.Tensor(
[[ 7.  8.]
[13. 11.]], shape=(2, 2), dtype=float64)
Result:  tf.Tensor(
[[0.65850461 0.75257669]
[0.76338629 0.64594224]], shape=(2, 2), dtype=float64)```

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