def argmin(input, dim=None, keepdim=False):
"""Returns the indices of the minimum values of a tensor across a dimension.
This is the second value returned by :meth:`torch.min`. See its
documentation for the exact semantics of this method.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce. If ``None``, the argmin of the
flattened input is returned.
keepdim (bool): whether the output tensors have :attr:`dim`
retained or not. Ignored if ``dim=None``.
Example::
>>> a = torch.randn(4, 4)
>>> a
2.3461 0.0056 1.4846 0.3911
-1.3584 -1.0066 0.0530 1.1754
-0.7929 -0.3194 -1.4865 0.4020
0.1101 0.6694 1.3456 0.8235
[torch.FloatTensor of size (4,4)]
>>> torch.argmin(a, dim=1)
1
0
2
0
[torch.LongTensor of size (4,)]
"""
if dim is None:
return torch._argmin(input.contiguous().view(-1), dim=0, keepdim=False)
return torch._argmin(input, dim, keepdim)
def argmin(input, dim=None, keepdim=False):
r"""Returns the indices of the minimum values of a tensor across a dimension.
This is the second value returned by :meth:`torch.min`. See its
documentation for the exact semantics of this method.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce. If ``None``, the argmin of the
flattened input is returned.
keepdim (bool): whether the output tensors have :attr:`dim`
retained or not. Ignored if ``dim=None``.
Example::
>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.1139, 0.2254, -0.1381, 0.3687],
[ 1.0100, -1.1975, -0.0102, -0.4732],
[-0.9240, 0.1207, -0.7506, -1.0213],
[ 1.7809, -1.2960, 0.9384, 0.1438]])
>>> torch.argmin(a, dim=1)
tensor([ 2, 1, 3, 1])
"""
if dim is None:
return torch._argmin(input.contiguous().view(-1), dim=0, keepdim=False)
return torch._argmin(input, dim, keepdim)
def argmin(input, dim=None, keepdim=False):
"""Returns the indices of the minimum values of a tensor across a dimension.
This is the second value returned by :meth:`torch.min`. See its
documentation for the exact semantics of this method.
Args:
input (Tensor): the input tensor
dim (int): the dimension to reduce. If ``None``, the argmin of the
flattened input is returned.
keepdim (bool): whether the output tensors have :attr:`dim`
retained or not. Ignored if ``dim=None``.
Example::
>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.1139, 0.2254, -0.1381, 0.3687],
[ 1.0100, -1.1975, -0.0102, -0.4732],
[-0.9240, 0.1207, -0.7506, -1.0213],
[ 1.7809, -1.2960, 0.9384, 0.1438]])
>>> torch.argmin(a, dim=1)
tensor([ 2, 1, 3, 1])
"""
if dim is None:
return torch._argmin(input.contiguous().view(-1), dim=0, keepdim=False)
return torch._argmin(input, dim, keepdim)
