mindspore.ops.any
- mindspore.ops.any(input, axis=None, keep_dims=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 axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.
Note
The axis 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.
axis (Union[int, tuple(int), list(int), Tensor], optional) – The dimensions to reduce. Suppose the rank of input is r, axis must be in the range [-rank(input), rank(input)). Default:
None
, all dimensions are reduced.keep_dims (bool, optional) – If
True
, keep these reduced dimensions and the length is 1. IfFalse
, don't keep these dimensions. Default :False
.
- Returns
Tensor, the dtype is bool.
If axis is
None
, and keep_dims isFalse
, the output is a 0-D Tensor representing the "logical OR" of all elements in the input Tensor.If axis is int, such as 2, and keep_dims is
False
, the shape of output is \((input_1, input_3, ..., input_R)\).If axis is tuple(int), such as (2, 3), and keep_dims is
False
, the shape of output is \((input_1, input_4, ..., input_R)\).If axis is 1-D Tensor, such as [2, 3], and keep_dims is
False
, the shape of output is \((input_1, input_4, ..., input_R)\).
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([[True, False], [True, True]])) >>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension. >>> output = ops.any(x, keep_dims=True) >>> print(output) [[ True]] >>> print(output.shape) (1, 1) >>> # case 2: Reduces a dimension along axis 0. >>> output = ops.any(x, axis=0) >>> print(output) [ True True] >>> # case 3: Reduces a dimension along axis 1. >>> output = ops.any(x, axis=1) >>> print(output) [ True True]