mindspore.ops.ReduceAny
- class mindspore.ops.ReduceAny(keep_dims=False)[source]
Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension, by default. And also can reduce a dimension of x 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
keep_dims (bool) – If
True
, keep these reduced dimensions and the length is 1. IfFalse
, don't keep these dimensions. Default:False
.
- Inputs:
x (Tensor[bool]) - The input tensor. The dtype of the tensor to be reduced is bool.
axis (Union[int, tuple(int), list(int), Tensor]) - The dimensions to reduce. Default:
()
, reduce all dimensions. Only constant value is allowed. Must be in the range [-rank(x), rank(x)).
- Outputs:
Tensor, the dtype is bool.
If axis is
()
, 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, set as 2, and keep_dims is
False
, the shape of output is \((x_1, x_3, ..., x_R)\).If axis is tuple(int), set as (2, 3), and keep_dims is
False
, the shape of output is \((x_1, x_4, ..., x_R)\).If axis is 1-D Tensor, set as [2, 3], and keep_dims is
False
, the shape of output is \((x_1, x_4, ..., x_R)\).
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([[True, False], [True, True]])) >>> op = ops.ReduceAny(keep_dims=True) >>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension. >>> output = op(x) >>> print(output) [[ True]] >>> print(output.shape) (1, 1) >>> # case 2: Reduces a dimension along axis 0. >>> output = op(x, 0) >>> print(output) [[ True True]] >>> # case 3: Reduces a dimension along axis 1. >>> output = op(x, 1) >>> print(output) [[True] [ True]] >>> # case 4: input is a scalar. >>> x = Tensor(True) >>> op = ops.ReduceAny() >>> output = op(x) >>> print(output) True