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mindspore.mint.logical_xor

mindspore.mint.logical_xor(input, other)[source]

Computes the "logical XOR" of two tensors element-wise.

o u t_{i} = i n p u t_{i} \oplus o t h e r_{i}

Parameters

input (Tensor) – The first input is a tensor whose data type can be implicitly converted to bool.
other (Tensor) – The second input is a tensor whose data type can be implicitly converted to bool to compute XOR with the first input.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is bool.

Raises

TypeError – If the dtype of input or other is not bool or can not be implicitly converted to bool.
ValueError – If the shape of two inputs cannot be broadcast.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([True, False, True]), mindspore.bool_)
>>> y = Tensor(np.array([True, True, False]), mindspore.bool_)
>>> output = mint.logical_xor(x, y)
>>> print(output)
[False True True]
>>> x = Tensor(1, mindspore.bool_)
>>> y = Tensor(0, mindspore.bool_)
>>> output = mint.logical_xor(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(0, mindspore.bool_)
>>> output = mint.logical_xor(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(np.array([True, False]), mindspore.bool_)
>>> output = mint.logical_xor(x, y)
>>> print(output)
[False True]