[FIXED] `sklearn.preprocessing.normalize` (L2 norm) equivalent in Tensorflow or TFX

Issue

How can I do the L2 norm in Tensorflow? I'm looking for the equivalent of sklearn.preprocessing.normalize in Tensorflow or in tfx.

Solution

You can use tensorflow.keras.utils.normalize for L2 norm as follows.

Using sklearn.preprocessing.normalize

X = [[ 1., -1.,  2.],
     [ 2.,  0.,  0.],
     [ 0.,  1., -1.]]

X_normalized = sklearn.preprocessing.normalize(X, norm='l2')
X_normalized

Output:

array([[ 0.40824829, -0.40824829,  0.81649658],
       [ 1.        ,  0.        ,  0.        ],
       [ 0.        ,  0.70710678, -0.70710678]])

Using tf.keras.utils.normalize gives the same output as above

X = [[ 1., -1.,  2.],
     [ 2.,  0.,  0.],
     [ 0.,  1., -1.]]

tf.keras.utils.normalize(
    X, order=2
)

Output:

array([[ 0.40824829, -0.40824829,  0.81649658],
       [ 1.        ,  0.        ,  0.        ],
       [ 0.        ,  0.70710678, -0.70710678]])

Answered By - Tfer3