mindspore.ops.amin

mindspore.ops.amin(input, axis=None, keepdims=False, *, initial=None, where=None)[source]

Return the minimum values along the given axis of the tensor.

Warning

Starting after version 2.9.0, this API will undergo an incompatible change:

  • The parameter axis will be renamed to dim, with default value changing to ().

  • The parameter keepdims will be renamed to keepdim.

  • The keyword arguments initial and where will be removed.

The new signature will be mindspore.ops.amin(input, dim=(), keepdim=False).

Parameters

  • input (Tensor[Number]) – The input tensor.

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

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

Keyword Arguments

  • initial (scalar, optional) – Initial value for the minimum. Default None .

  • where (Tensor[bool], optional) – Specifies the range over which to compute the minimum values. The shape of this tensor must be broadcastable to the shape of input . An initial value must be specified. Default None , indicating that all elements are to be computed.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> input = mindspore.tensor([[2, 5, 1, 6],
...                           [3, -7, -2, 4],
...                           [8, -4, 1, -3]])
>>> # case 1: By default, compute the minimum of all elements.
>>> mindspore.ops.amin(input)
Tensor(shape=[], dtype=Int64, value= -7)
>>>
>>> # case 2: Compute minimum along axis 1.
>>> mindspore.ops.amin(input, axis=1)
Tensor(shape=[3], dtype=Int64, value= [ 1, -7, -4])
>>>
>>> # case 3: If keepdims=True, the output shape will be the same as that of the input.
>>> mindspore.ops.amin(input, axis=1, keepdims=True)
Tensor(shape=[3, 1], dtype=Int64, value=
[[ 1],
 [-7],
 [-4]])
>>>
>>> # case 4: Use "where" to include only specific elements in computing the minimum.
>>> where = mindspore.tensor([[1, 0, 1, 0],
...                           [0, 0, 1, 1],
...                           [1, 1, 1, 0]], dtype=mindspore.bool)
>>> mindspore.ops.amin(input, axis=1, keepdims=True, initial=0, where=where)
Tensor(shape=[3, 1], dtype=Int64, value=
 [[ 0],
  [-2],
  [-4]])
>>>
>>> # case 5: The shape of "where" must be broadcast compatible with input.
>>> where = mindspore.tensor([[False],
...                           [False],
...                           [False]])
>>> mindspore.ops.amin(input, axis=0, keepdims=True, initial=0, where=where)
Tensor(shape=[1, 4], dtype=Int64, value=
 [[0, 0, 0, 0]])