Frobenius Norm is somtimes called the Hilbert-Schmidt norm, in this tutorial, we will introduce some basic knowlege for deep learning beginners.
The formula of Frobenius Norm
Frobenius Norm is defined as:
where A is a m*n matrix. I can find the value of frobenius norm is a scalar.
How to calculate the value of frobenius norm?
It is easy to compute frobenius norm in numpy, here is an example:
import numpy as np A = np.array([[1, 2, 3],[4, 5, 6]]) F = np.linalg.norm(A) print(F)
In this example, A is a 2*3 matrix, we can use numpy.linalg.norm() to calculate its frobenius norm value, the value is:
9.53939201417
Feature of Frobenius Norm
General properties of frobenius norm are:
