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
- Returns
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> 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]