Returns the matrix norm or vector norm of a given tensor.
Warning
torch.norm is deprecated and may be removed in a future PyTorch release. Its documentation and behavior may be incorrect, and it is no longer actively maintained.
Use torch.linalg.vector_norm() when computing vector norms and torch.linalg.matrix_norm() when computing matrix norms. For a function with a similar behavior as this one see torch.linalg.norm(). Note, however, the signature for these functions is slightly different than the signature for torch.norm.
Note
Even though p='fro' supports any number of dimensions, the true mathematical definition of Frobenius norm only applies to tensors with exactly two dimensions. torch.linalg.matrix_norm() with ord='fro' aligns with the mathematical definition, since it can only be applied across exactly two dimensions.
>>> import torch
>>> a = torch.arange(9, dtype= torch.float) - 4
>>> b = a.reshape((3, 3))
>>> torch.norm(a)
tensor(7.7460)
>>> torch.norm(b)
tensor(7.7460)
>>> torch.norm(a, float('inf'))
tensor(4.)
>>> torch.norm(b, float('inf'))
tensor(4.)
>>> c = torch.tensor([[ 1, 2, 3], [-1, 1, 4]] , dtype=torch.float)
>>> torch.norm(c, dim=0)
tensor([1.4142, 2.2361, 5.0000])
>>> torch.norm(c, dim=1)
tensor([3.7417, 4.2426])
>>> torch.norm(c, p=1, dim=1)
tensor([6., 6.])
>>> d = torch.arange(8, dtype=torch.float).reshape(2, 2, 2)
>>> torch.norm(d, dim=(1, 2))
tensor([ 3.7417, 11.2250])
>>> torch.norm(d[0, :, :]), torch.norm(d[1, :, :])
(tensor(3.7417), tensor(11.2250))
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4