mindspore.mint.ne

mindspore.mint.ne(input, other)[source]

Computes the non-equivalence of two tensors element-wise.

Note

  • Inputs of input and other comply with the implicit type conversion rules to make the data types consistent.

  • When the inputs are two tensors, the shapes of them could be broadcast.

  • When the inputs are one tensor and one scalar, the scalar could only be a constant.

  • Broadcasting is supported.

\[\begin{split}out_{i} =\begin{cases} & \text{True, if } input_{i} \ne other_{i} \\ & \text{False, if } input_{i} = other_{i} \end{cases}\end{split}\]
Parameters

  • input (Union[Tensor, Number, bool]) – The first input is a number or a bool or a tensor whose data type is number or bool.

  • other (Union[Tensor, Number, bool]) – The second input is a number or a bool when the first input is a tensor or a tensor whose data type is number or bool.

Returns

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

Raises

TypeError – If input and other is not one of the following: Tensor, Number, bool.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> x = Tensor([1, 2, 3], mindspore.float32)
>>> output = mint.ne(x, 2.0)
>>> print(output)
[ True False  True]
>>>
>>> x = Tensor([1, 2, 3], mindspore.int32)
>>> y = Tensor([1, 2, 4], mindspore.int32)
>>> output = mint.ne(x, y)
>>> print(output)
[False False  True]