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 libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([7, 8, 13, 11], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating the resultres = tf.math.l2_normalize(a)# Printing the resultprint('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 libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([[7, 8], [13, 11]], dtype = tf.float64)# Printing the input tensorprint('a: ', a)# Calculating the resultres = tf.math.l2_normalize(x = a, axis = 1)# Printing the resultprint('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)
