mindspore.ops.ArgMinWithValue

class mindspore.ops.ArgMinWithValue(axis=0, keep_dims=False)[source]

Calculates the minimum value along with the given axis for the input tensor, and returns the minimum values and indices.

Note

In auto_parallel and semi_auto_parallel mode, the first output index can not be used.

Warning

  • If there are multiple minimum values, the index of the first minimum value is used.

  • The value range of “axis” is [-dims, dims - 1]. “dims” is the dimension length of “x”.

Also see: func: mindspore.ops.min.

Parameters

  • axis (int) – The dimension to reduce. Default: 0.

  • keep_dims (bool) – Whether to reduce dimension, if true the output will keep the same dimension as the input, the output will reduce dimension if false. Default: False.

Inputs:

  • x (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as \((x_1, x_2, ..., x_N)\) .Complex tensor is not supported.

Outputs:

tuple (Tensor), tuple of 2 tensors, containing the corresponding index and the minimum value of the input tensor.

  • index (Tensor) - The index for the minimum value of the input tensor, with dtype int32. If keep_dims is true, the shape of output tensors is \((x_1, x_2, ..., x_{axis-1}, 1, x_{axis+1}, ..., x_N)\). Otherwise, the shape is \((x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)\) .

  • values (Tensor) - The minimum value of input tensor, with the same shape as index, and same dtype as x.

Raises
Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> index, output = ops.ArgMinWithValue()(x)
>>> print(index, output)
0 0.0
>>> index, output = ops.ArgMinWithValue(keep_dims=True)(x)
>>> print(index, output)
[0] [0.0]