mindspore.ops.ArgMinWithValue

class mindspore.ops.ArgMinWithValue(*args, **kwargs)[source]

Calculates the minimum value with corresponding index, and returns indices and values.

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

Note

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

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:

input_x (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as \((x_1, x_2, ..., x_N)\).

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. 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 \((x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)\). - output_x (Tensor) - The minimum value of input tensor, with the same shape as index.

Raises

TypeError – If keep_dims is not a bool.
TypeError – If axis is not an int.

Supported Platforms:: Ascend CPU

Examples

>>> input_x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output = ops.ArgMinWithValue()(input_x)
>>> print(output)
(Tensor(shape=[], dtype=Int32, value= 0), Tensor(shape=[], dtype=Float32, value= 0.0))
>>> output = ops.ArgMinWithValue(keep_dims=True)(input_x)
>>> print(output)
(Tensor(shape=[1], dtype=Int32, value= [0]), Tensor(shape=[1], dtype=Float32, value= [0.0]))