mindspore.Tensor.argmin

Tensor.argmin(axis=None, keepdims=False) → Tensor

Returns the indices of the minimum values along the given axis of the tensor.

Parameters

  • axis (Union[int, None], optional) – Specify the axis for computation. If None , compute all elements in the tensor. Default None .

  • keepdims (bool, optional) – Whether the output tensor has dim retained. Default False .

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> x = mindspore.tensor([[2, 5, 1, 6],
...                       [3, -7, -2, 4],
...                       [8, -4, 1, -3]])
>>> # case 1: By default, compute the minimum of all elements.
>>> x.argmin()
Tensor(shape=[], dtype=Int32, value= 5)
>>>
>>> # case 2: Compute the minimum along axis 1.
>>> x.argmin(axis=1)
Tensor(shape=[3], dtype=Int32, value= [2, 1, 1])
>>>
>>> # case 3: If keepdims=True, the output shape will be same of that of the input.
>>> x.argmin(axis=1, keepdims=True)
Tensor(shape=[3, 1], dtype=Int32, value=
[[2],
 [1],
 [1]])
Tensor.argmin(dim=None, keepdim=False) → Tensor

Returns the indices of the minimum values along the given axis of the tensor.

Parameters

  • dim (Union[int, None], optional) – Specify the axis for computation. If None , compute all elements in the tensor.

  • keepdim (bool, optional) – Whether the output tensor has dim retained.

Returns

Tensor

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> x = mindspore.tensor([[2, 5, 1, 6],
...                       [3, -7, -2, 4],
...                       [8, -4, 1, -3]])
>>> # case 1: By default, compute the minimum of all elements.
>>> x.argmin()
Tensor(shape=[], dtype=Int32, value= 5)
>>>
>>> # case 2: Compute the minimum along dim 1.
>>> x.argmin(dim=1)
Tensor(shape=[3], dtype=Int32, value= [2, 1, 1])
>>>
>>> # case 3: If keepdim=True, the output shape will be same of that of the input.
>>> x.argmin(dim=1, keepdim=True)
Tensor(shape=[3, 1], dtype=Int32, value=
[[2],
 [1],
 [1]])