- paddle. logical_xor ( x, y, out=None, name=None )
logical_xoroperator computes element-wise logical XOR on
y, and returns
outis N-dim boolean
Tensor. Each element of
outis calculated by\[out = (x || y) \&\& !(x \&\& y)\]
paddle.logical_xorsupports broadcasting. If you want know more about broadcasting, please refer to Broadcasting.
x (Tensor) – the input tensor, it’s data type should be one of bool, int8, int16, in32, in64, float32, float64.
y (Tensor) – the input tensor, it’s data type should be one of bool, int8, int16, in32, in64, float32, float64.
out (Tensor) – The
Tensorthat specifies the output of the operator, which can be any
Tensorthat has been created in the program. The default value is None, and a new
Tensorwill be created to save the output.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
N-D Tensor. A location into which the result is stored. It’s dimension equals with
import paddle import numpy as np x_data = np.array([True, False], dtype=np.bool).reshape([2, 1]) y_data = np.array([True, False, True, False], dtype=np.bool).reshape([2, 2]) x = paddle.to_tensor(x_data) y = paddle.to_tensor(y_data) res = paddle.logical_xor(x, y) print(res) # [[False, True], [ True, False]]