Min
Arguments
- self
(Tensor) the input tensor.
- dim
(int) the dimension to reduce.
- keepdim
(bool) whether the output tensor has
dimretained or not.- out
(tuple, optional) the tuple of two output tensors (min, min_indices)
- other
(Tensor) the second input tensor
Note
When the shapes do not match, the shape of the returned output tensor follows the broadcasting rules .
min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)
Returns a namedtuple (values, indices) where values is the minimum
value of each row of the input tensor in the given dimension
dim. And indices is the index location of each minimum value found
(argmin).
Warning
indices does not necessarily contain the first occurrence of each
minimal value found, unless it is unique.
The exact implementation details are device-specific.
Do not expect the same result when run on CPU and GPU in general.
If keepdim is TRUE, the output tensors are of the same size as
input except in the dimension dim where they are of size 1.
Otherwise, dim is squeezed (see torch_squeeze), resulting in
the output tensors having 1 fewer dimension than input.
min(input, other, out=NULL) -> Tensor
Each element of the tensor input is compared with the corresponding
element of the tensor other and an element-wise minimum is taken.
The resulting tensor is returned.
The shapes of input and other don't need to match,
but they must be broadcastable .
$$ \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i) $$
Examples
if (torch_is_installed()) {
a = torch_randn(c(1, 3))
a
torch_min(a)
a = torch_randn(c(4, 4))
a
torch_min(a, dim = 1)
a = torch_randn(c(4))
a
b = torch_randn(c(4))
b
torch_min(a, other = b)
}
#> torch_tensor
#> -0.3243
#> 0.2041
#> -1.7173
#> 0.4610
#> [ CPUFloatType{4} ]