mindspore.mint.all
- mindspore.mint.all(input, dim=None, keepdim=False)[source]
Reduces a dimension of input by the "logical AND" 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. IfFalse
, don't keep these dimensions. Default :False
.
- Returns
Tensor, the dtype is bool.
If dim is
None
, and keepdim isFalse
, the output is a 0-D Tensor representing the "logical AND" 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
- 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 "logicalAND" of all elements in the dimension. >>> output = mint.all(x, keepdim=True) >>> print(output) [[False]] >>> print(output.shape) (1, 1) >>> # case 2: Reduces a dimension along axis 0. >>> output = mint.all(x, dim=0) >>> print(output) [ True False] >>> # case 3: Reduces a dimension along axis 1. >>> output = mint.all(x, dim=1) >>> print(output) [False True]