mindspore.ops.DivNoNan

class mindspore.ops.DivNoNan[源代码]

Computes a safe divide and returns 0 if the x2 is zero.

Inputs of x1 and x2 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, dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

\[\begin{split}output_{i} = \begin{cases} 0, & \text{ if } x2_{i} = 0\\ x1_{i} / x2_{i}, & \text{ if } x2_{i} \ne 0 \end{cases}\end{split}\]

Inputs:

x1 (Union[Tensor, number.Number, bool]) - The first input is a number.Number or a bool or a tensor whose data type is number or bool_.
x2 (Union[Tensor, number.Number, bool]) - The second input is a number.Number or a bool when the first input is a tensor or a tensor whose data type is number or bool_. When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.

Outputs:

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises: TypeError – If x1 and x2 is not a number.Number or a bool or a Tensor.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x1 = Tensor(np.array([-1.0, 0., 1.0, 5.0, 6.0]), mindspore.float32)
>>> x2 = Tensor(np.array([0., 0., 0., 2.0, 3.0]), mindspore.float32)
>>> div_no_nan = ops.DivNoNan()
>>> output = div_no_nan(x1, x2)
>>> print(output)
[0.  0.  0.  2.5 2. ]