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Specifications and Common Mistakes

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mindspore.Tensor.argmax_with_value

Tensor.argmax_with_value(axis=0, keep_dims=False)

Returns the maximum value with corresponding index.

Compute the max value of input Tensor on the specified axis, and return the max value and index.

Note

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

• 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 this tensor.

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 .

Returns

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. 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 $(x_1,x_2,...,x_{axis-1},x_{axis+1},...,x_N)$ .

• value (Tensor) - The maximum 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 GPU CPU

Examples

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