mindspore.ops.LogicalXor
- class mindspore.ops.LogicalXor[source]
Computes the “logical XOR” of two tensors element-wise.
Warning
This is an experimental API that is subject to change or deletion.
Refer to
mindspore.ops.logical_xor()
for more details.- Inputs:
x (Union[Tensor, bool]) - The first input is a bool or a tensor whose data type can be implicitly converted to bool.
y (Union[Tensor, bool]) - The second input is a bool when the first input is a tensor or a tensor whose data type can be implicitly converted to bool.
- Outputs:
Tensor, the shape is the same as the x and y after broadcasting, and the data type is bool.
- Supported Platforms:
Ascend
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.array([True, False, True]), mindspore.bool_) >>> y = Tensor(np.array([True, True, False]), mindspore.bool_) >>> logical_xor = ops.LogicalXor() >>> output = logical_xor(x, y) >>> print(output) [ False True True] >>> x = Tensor(1, mindspore.bool_) >>> y = Tensor(0, mindspore.bool_) >>> output = ops.LogicalXor()(x, y) >>> print(output) True >>> x = True >>> y = Tensor(0, mindspore.bool_) >>> output = ops.LogicalXor()(x, y) >>> print(output) True >>> x = True >>> y = Tensor(np.array([True, False]), mindspore.bool_) >>> output = ops.LogicalXor()(x, y) >>> print(output) [False True]