mindspore.Tensor.argmax

Tensor.argmax(axis=None, keepdims=False) → Tensor

Return the indices of the maximum values of a tensor across a dimension.

Parameters

axis (Union[int, None], optional) – The dimension to reduce. If axis is None , the indices of the maximum value within the flattened input will be returned. The value of axis cannot exceed the dimension of self. Default: None .
keepdims (bool, optional) – Whether the output tensor retains the specified dimension. Ignored if dim is None. Default: False .

Returns

Tensor, indices of the maximum values across a dimension.

Raises

TypeError – If keepdims is not bool.
ValueError – If axis is out of range.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([[1, 20, 5], [67, 8, 9], [130, 24, 15]]).astype(np.float32))
>>> output = Tensor.argmax(x, axis=-1) # x.argmax(axis=-1)
>>> print(output)
[1 0 0]

Tensor.argmax(dim=None, keepdim=False) → Tensor

Parameters

dim (Union[int, None], optional) – The dimension to reduce. If axis is None , the indices of the maximum value within the flattened input will be returned. The value of axis cannot exceed the dimension of self. Default: None .
keepdim (bool, optional) – Whether the output tensor retains the specified dimension. Ignored if dim is None. Default: False .

Returns

Tensor, indices of the maximum values across a dimension.

Raises

TypeError – If keepdim is not bool.
ValueError – If dim is out of range.

Supported Platforms:: Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([[1, 20, 5], [67, 8, 9], [130, 24, 15]]).astype(np.float32))
>>> output = Tensor.argmax(x, dim=-1) # x.argmax(dim=-1)
>>> print(output)
[1 0 0]