linalg.vector_norm in PyTorch

#pytorch #norm #vector #regularization

*Memos:

My post explains linalg.norm().
My post explains linalg.matrix_norm().

linalg.vector_norm() can get the 0D or more D tensor of the one or more elements computed with vector norm from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

linalg.vector_norm() can be used with torch but not with a tensor.
The 1st argument with torch is input or x(Required-Type:tensor of float or complex): *Memos:
- It must be the 0D or more D tensor of zero or more elements.
- A complex tensor returns a float tensor even if dtype=torch.complex64 is set to linalg.vector_norm() which is a bug.
The 2nd argument with torch is ord(Optional-Default:2-Type:int or float). *It sets a norm.
The 3rd argument with torch is dim(Optional-Default:None-Type:int or tuple or list of int).
The 4th argument with torch is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
- If it's None, it's inferred from input or x.
- dtype= must be used.
- My post explains dtype argument.
There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
If dim is None, the input(x) tensor is flattened to a 1D tensor.
ord supports the following norms: *Memos:
- inf can be torch.inf, float('inf'), etc:
- There are also L3 norm(ord=3), L4 norm(ord=4) etc.

`ord`	Vector norm
`inf`	`max(abs(x))`
`-inf`	`min(abs(x))`
`0`	`sum(x != 0)`(L0 norm)
`1`	`sum(abs(x)^{ord})^{(1/ord)}`(L1 norm)
`-1`	Same as above
`2`(Default)	Same as above(L2 norm)
`-2`	Same as above
Other `int` or `float`	Same as above

import torch
from torch import linalg

my_tensor = torch.tensor([-3., -2., -1., 0., 1., 2., 3., 4.])

linalg.vector_norm(input=my_tensor) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=0) # L0 norm
# tensor(7.)

linalg.vector_norm(input=my_tensor, ord=1) # L1 norm
# tensor(16.)

linalg.vector_norm(input=my_tensor, ord=-1)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=2) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=-2)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=torch.inf)
# tensor(4.)

linalg.vector_norm(input=my_tensor, ord=-torch.inf)
# tensor(0.)

my_tensor = torch.tensor([[-3., -2., -1., 0.],
                          [1., 2., 3., 4.]])
linalg.vector_norm(input=my_tensor) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=0) # L0 norm
# tensor(7.)

linalg.vector_norm(input=my_tensor, ord=1) # L1 norm
# tensor(16.)

linalg.vector_norm(input=my_tensor, ord=-1)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=2) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=-2)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=torch.inf)
# tensor(4.)

linalg.vector_norm(input=my_tensor, ord=-torch.inf)
# tensor(0.)

my_tensor = torch.tensor([[[-3., -2.], [-1., 0.]],
                          [[1., 2.], [3., 4.]]])
linalg.vector_norm(input=my_tensor) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=0) # L0 norm
# tensor(7.)

linalg.vector_norm(input=my_tensor, ord=0, dim=2) # L0 norm
linalg.vector_norm(input=my_tensor, ord=0, dim=-1) # L0 norm
# tensor([[2., 1.],
#         [2., 2.]])

linalg.vector_norm(input=my_tensor, ord=1) # L1 norm
# tensor(16.)

linalg.vector_norm(input=my_tensor, ord=1, dim=2) # L1 norm
linalg.vector_norm(input=my_tensor, ord=1, dim=-1) # L1 norm
# tensor([[5., 1.],
#         [3., 7.]])

linalg.vector_norm(input=my_tensor, ord=-1)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=-1, dim=2)
linalg.vector_norm(input=my_tensor, ord=-1, dim=-1)
# tensor([[1.2000, 0.0000],
#         [0.6667, 1.7143]])

linalg.vector_norm(input=my_tensor, ord=2) # L2 norm
# tensor(6.6332)

linalg.vector_norm(input=my_tensor, ord=2, dim=2) # L2 norm
linalg.vector_norm(input=my_tensor, ord=2, dim=-1) # L2 norm
# tensor([[3.6056, 1.0000],
#         [2.2361, 5.0000]])

linalg.vector_norm(input=my_tensor, ord=-2)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=-2, dim=2)
linalg.vector_norm(input=my_tensor, ord=-2, dim=-1)
# tensor([[1.6641, 0.0000],
#         [0.8944, 2.4000]])

linalg.vector_norm(input=my_tensor, ord=torch.inf)
# tensor(4.)

linalg.vector_norm(input=my_tensor, ord=torch.inf, dim=2)
linalg.vector_norm(input=my_tensor, ord=torch.inf, dim=-1)
# tensor([[3., 1.],
#         [2., 4.]])

linalg.vector_norm(input=my_tensor, ord=-torch.inf)
# tensor(0.)

linalg.vector_norm(input=my_tensor, ord=-torch.inf, dim=2)
linalg.vector_norm(input=my_tensor, ord=-torch.inf, dim=-1)
# tensor([[2., 0.],
#         [1., 3.]])

my_tensor = torch.tensor([[[-3.+0.j, -2.+0.j], [-1.+0.j, 0.+0.j]],
                          [[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]])
linalg.vector_norm(input=my_tensor, dtype=torch.complex64) # L2 norm
# tensor(6.6332)

DEV Community

linalg.vector_norm in PyTorch

Top comments (0)

Read next

Arrays

SplitView in iOS 18 - #30DaysOfSwift

Apredendo construção de apis com node e TypeScript

Modern HTML5 Boilerplate