# Python – tensorflow.math.l2_normalize()

• Last Updated : 16 Feb, 2022

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 library` `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 library` `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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