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mindspore.ops.ne

mindspore.ops.ne(x, y)

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.

$$\begin{array}{r}ou{t}_i=\{\begin{array}{ll}& \text{True, if }{x}_i\ne y_i\\ & \text{False, if }{x}_i=y_i\end{array}\end{array}$$

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

>>> 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]