min¶
Documentation¶
-
treetensor.torch.
min
(input, *args, reduce=None, **kwargs)[source]¶ In
treetensor
, you can get themin
result of a whole tree with this function.Example:
>>> import torch >>> import treetensor.torch as ttorch >>> ttorch.min(torch.tensor([1.0, 2.0, 1.5])) # the same as torch.min tensor(1.) >>> ttorch.min(ttorch.tensor({ ... 'a': [1.0, 2.0, 1.5], ... 'b': {'x': [[1.8, 0.9], [1.3, 2.5]]}, ... })) tensor(0.9000) >>> ttorch.min(ttorch.tensor({ ... 'a': [1.0, 2.0, 1.5], ... 'b': {'x': [[1.8, 0.9], [1.3, 2.5]]}, ... }), reduce=False) <Tensor 0x7fd45b5913c8> ├── a --> tensor(1.) └── b --> <Tensor 0x7fd45b5912e8> └── x --> tensor(0.9000) >>> ttorch.min(ttorch.tensor({ ... 'a': [1.0, 2.0, 1.5], ... 'b': {'x': [[1.8, 0.9], [1.3, 2.5]]}, ... }), dim=0) torch.return_types.min( values=<Tensor 0x7fd45b52d2e8> ├── a --> tensor(1.) └── b --> <Tensor 0x7fd45b52d208> └── x --> tensor([1.3000, 0.9000]) , indices=<Tensor 0x7fd45b591cc0> ├── a --> tensor(0) └── b --> <Tensor 0x7fd45b52d3c8> └── x --> tensor([1, 0]) )
Torch Version Related
This documentation is based on torch.min in torch v1.10.0+cu102. Its arguments’ arrangements depend on the version of pytorch you installed.
If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.
1 | python -c 'import torch;print(torch.__version__)' |
The arguments and keyword arguments supported in torch v1.10.0+cu102 is listed below.
Description From Torch v1.10.0+cu102¶
-
torch.
min
(input) → Tensor¶ Returns the minimum value of all elements in the
input
tensor.Warning
This function produces deterministic (sub)gradients unlike
min(dim=0)
- Args:
input (Tensor): the input tensor.
Example:
>>> a = torch.randn(1, 3) >>> a tensor([[ 0.6750, 1.0857, 1.7197]]) >>> torch.min(a) tensor(0.6750)
-
torch.
min
(input, dim, keepdim=False, *, out=None)
Returns a namedtuple
(values, indices)
wherevalues
is the minimum value of each row of theinput
tensor in the given dimensiondim
. Andindices
is the index location of each minimum value found (argmin).If
keepdim
isTrue
, the output tensors are of the same size asinput
except in the dimensiondim
where they are of size 1. Otherwise,dim
is squeezed (seetorch.squeeze()
), resulting in the output tensors having 1 fewer dimension thaninput
.Note
If there are multiple minimal values in a reduced row then the indices of the first minimal value are returned.
- Args:
input (Tensor): the input tensor. dim (int): the dimension to reduce. keepdim (bool): whether the output tensor has
dim
retained or not.- Keyword args:
out (tuple, optional): the tuple of two output tensors (min, min_indices)
Example:
>>> a = torch.randn(4, 4) >>> a tensor([[-0.6248, 1.1334, -1.1899, -0.2803], [-1.4644, -0.2635, -0.3651, 0.6134], [ 0.2457, 0.0384, 1.0128, 0.7015], [-0.1153, 2.9849, 2.1458, 0.5788]]) >>> torch.min(a, 1) torch.return_types.min(values=tensor([-1.1899, -1.4644, 0.0384, -0.1153]), indices=tensor([2, 0, 1, 0]))
-
torch.
min
(input, other, *, out=None) → Tensor
See
torch.minimum()
.