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.
keep_dims (bool) – If true, keep these reduced dimensions and the length is 1. If false, don’t keep these dimensions. Default : False.
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 be 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)).
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 \((x_1, x_4, ..., x_R)\).
- Supported Platforms:
>>> 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]]