mindspore.Tensor.argmin

Tensor.argmin(axis=None, keepdims=False) → Tensor

Returns the indices of the minimum value of self across the axis.

If the shape of self is \((self_1, ..., self_N)\), the shape of the output tensor is \((self_1, ..., self_{axis-1}, self_{axis+1}, ..., self_N)\).

Parameters

• axis (Union[int, None], optional) – Axis where the Argmin operation applies to. If None, it will return the index of the minimum value in the flattened Tensor along the specified axis. Default: None .

• keepdims (bool, optional) – Whether the output tensor retains the specified dimension. Ignored if axis is None. Default: False .

Returns

Tensor, indices of the min value of self across the axis.

Raises

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([2.0, 3.1, 1.2]), mindspore.float32)
>>> index = Tensor.argmin(input_x) # input_x.argmin()
>>> print(index)
2

Tensor.argmin(dim=None, keepdim=False) → Tensor

Returns the indices of the minimum value of self across the dim.

If the shape of self is \((self_1, ..., self_N)\), the shape of the output tensor is \((self_1, ..., self_{dim-1}, self_{dim+1}, ..., self_N)\).

Parameters

• dim (Union[int, None], optional) – Dimension where the Argmin operation applies to. If None, it will return the index of the minimum value in the flattened Tensor along the specified dimension. 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 min value of self across the dimension.

Raises

TypeError – If dim is not an int.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32)
>>> index = Tensor.argmin(input_x) # input_x.argmin()
>>> print(index)
2