mindspore.ops.ne

mindspore.ops.ne(x, y)[source]

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

Note

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

  • The inputs must be two tensors or one tensor and one scalar.

  • 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.

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

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

  • y (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 x and y is not one of the following: Tensor, Number, bool.

  • TypeError – If neither x nor y is a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

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