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mindspore.ops.MulNoNan

class mindspore.ops.MulNoNan

Computes x * y element-wise. If y is zero, no matter what x is, it will return 0.

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

$$\begin{array}{r}outpu{t}_{ij}=\{\begin{array}{ll}0,& y_{ij}=0;\\ x_{ij}\ast y_{ij},& otherwise.\end{array}\end{array}$$

Note

The shapes of x and y should be the same or can be broadcasted. This is noncommutative: if y is NaN or infinite and x is 0, the result will be NaN.

Inputs:

• x (Union[Tensor]) - The first input is a tensor whose data type is one of int32, int64, float16, float32, float64, complex64, complex128 currently or scalar.

• y (Union[Tensor]) - The second input is a tensor whose data type is one of int32, int64, float16, float32, float64, complex64, complex128 currently or scalar.

Outputs:

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

Raises

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
>>> # case 1 : same data type and shape of two inputs, there are some 0 in y.
>>> x = Tensor(np.array([[-1.0, 6.0, np.inf], [np.nan, -7.0, 4.0]]), mindspore.float32)
>>> y = Tensor(np.array([[-1.0, 4.0, 0], [0, -3.0, 1.0]]), mindspore.float32)
>>> mul_no_nan = ops.MulNoNan()
>>> output = mul_no_nan(x, y)
>>> print(output)
[[ 1. 24. 0.]
[ 0. 21. 4.]]
>>> # case 2 : the shape of two inputs is same, there are some 0 in x, y.
>>> x = Tensor(np.array([[-1.0, 6.0, 0], [0, np.nan, 4.0]]), mindspore.float32)
>>> y = Tensor(np.array([[-1.0, 4.0, np.inf], [np.nan, 0, 1.0]]), mindspore.float32)
>>> output = mul_no_nan(x, y)
>>> print(output)
[[ 1. 24. nan]
 [nan  0. 4.]]
>>> print(output.dtype)
Float32
>>> # case 3 : the y is a scalar.
>>> x = Tensor(np.array([[-1.0, 6.0, 0], [0, np.nan, 4.0]]), mindspore.float32)
>>> y = Tensor(0, mindspore.float32)
>>> output = mul_no_nan(x, y)
>>> print(output)
[[0. 0. 0.]
 [0. 0. 0.]]