mindspore.mint.any

View Source On Gitee
mindspore.mint.any(input, dim=None, keepdim=False)[source]

Reduces a dimension of input by the "logical OR" of all elements in the dimension, by default. And also can reduce a dimension of input along the dim. Determine whether the dimensions of the output and input are the same by controlling keepdim.

Note

The dim with tensor type is only used for compatibility with older versions and is not recommended.

Parameters

  • input (Tensor) – Input Tensor, has the shape \((N, *)\) where \(*\) means, any number of additional dimensions.

  • dim (Union[int, tuple(int), list(int), Tensor], optional) – The dimensions to reduce. Suppose the rank of input is r, dim must be in the range [-rank(input), rank(input)). Default: None , all dimensions are reduced.

  • keepdim (bool, optional) – If True , keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default : False .

Returns

Tensor, the dtype is bool.

  • If dim is None , and keepdim is False , the output is a 0-D Tensor representing the "logical OR" of all elements in the input Tensor.

  • If dim is int, such as 2, and keepdim is False , the shape of output is \((input_1, input_3, ..., input_R)\).

  • If dim is tuple(int), such as (2, 3), and keepdim is False , the shape of output is \((input_1, input_4, ..., input_R)\).

  • If dim is 1-D Tensor, such as [2, 3], and keepdim is False , the shape of output is \((input_1, input_4, ..., input_R)\).

Raises

  • TypeError – If keepdim is not a bool.

  • TypeError – If input is not a Tensor.

  • TypeError – If dim is not one of the following: int, tuple, list or Tensor.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension.
>>> output = mint.any(x, keepdim=True)
>>> print(output)
[[ True]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along dim 0.
>>> output = mint.any(x, dim=0)
>>> print(output)
[ True True]
>>> # case 3: Reduces a dimension along dim 1.
>>> output = mint.any(x, dim=1)
>>> print(output)
[ True True]