mindspore.ops.ArgMaxWithValue

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

Calculates the maximum value along with the given axis for the input tensor, and returns the maximum 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 maximum values, the index of the first maximum value is used.
The value range of axis is [-dims, dims - 1]. "dims" is the dimension length of input.

Also see mindspore.ops.max().

Parameters

axis (int) – The dimension to reduce. Default: 0 .
keep_dims (bool) – Whether to reduce dimension, if True , the output will keep same dimension with the input, the output will reduce dimension if false . Default: False .

Inputs:

input (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as \((input_1, input_2, ..., input_N)\).

Outputs:

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

index (Tensor) - The index for the maximum value of the input tensor, with dtype int64. If keep_dims is True , the shape of output tensors is \((input_1, input_2, ..., input_{axis-1}, 1, input_{axis+1}, ..., input_N)\). Otherwise, the shape is \((input_1, input_2, ..., input_{axis-1}, input_{axis+1}, ..., input_N)\) .
values (Tensor) - The maximum value of input tensor, with the same shape as index, and same dtype as input.

Raises

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

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> index, output = ops.ArgMaxWithValue()(input_x)
>>> print(index, output)
3 0.7
>>> index, output = ops.ArgMaxWithValue(keep_dims=True)(input_x)
>>> print(index, output)
[3] [0.7]