mindspore.ops.ReduceAll

class mindspore.ops.ReduceAll(keep_dims=False)[source]

Reduces a dimension of a tensor by the “logicalAND” 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.

Parameters: keep_dims (bool) – If True , keep these reduced dimensions and the length is 1. If False , 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)]) - 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 is False , the output is a 0-D tensor representing the “logical and” 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 \((x_1, x_4, ..., x_R)\).

Raises

TypeError – If keep_dims is not a bool.
TypeError – If x is not a Tensor.
TypeError – If axis is not one of the following: int, tuple or list.

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.ReduceAll(keep_dims=True)
>>> # case 1: Reduces a dimension by the "logicalAND" of all elements in the dimension.
>>> output = op(x)
>>> print(output)
[[False]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = op(x, 0)
>>> print(output)
[[ True False]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = op(x, 1)
>>> print(output)
[[False]
[ True]]
>>> # case 4: input is a scalar.
>>> x = Tensor(True)
>>> op = ops.ReduceAll()
>>> output = op(x)
>>> print(output)
True