torch norm() Formalize()

The function of normalize in torch is to unitize a vector. For example, a vector is:

x = [1,2,3,4]

The standardization process is to first find the second norm of this vector, and then divide the value of each dimension by the second norm.

import torch.nn.functional as F
import torch

x = torch.randn(1,3)
print(x)

z = F.normalize(x)
print(z)

The running result is:

tensor([[-1.0407, -1.1139, -0.9541]])

Let a vector be x, then x.norm() returns the second norm of this vector

import torch

x = torch.tensor([1.0,2.0,3.0])
print(x.norm())

The output is:

tensor(3.7417)

So if you want to unitize a vector, you can directly F.normalize(x), or x = x / x.norm()