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 reduces 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, don’t keep these reduced dimensions.

Inputs:

  • x (Tensor[bool]) - The input tensor. The dtype of the tensor to be reduced is bool. \((N,*)\) where \(*\) means, any number of additional dimensions, its rank should less than 8.

  • 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.

  • ValueError – If axis is not one of the following: int, tuple or list.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> op = ops.ReduceAll(keep_dims=True)
>>> # case 1: Reduces a dimension by averaging 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]]