mindspore.ops.MulNoNan

class mindspore.ops.MulNoNan[source]

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

Note

The shapes of x and y should be the same or can be broadcasted.

Inputs:

x (Union[Tensor]) - The first input is a tensor whose data type is one of flota16, float32, int32, int64 currently or scalar.
y (Union[Tensor]) - The second input is a tensor whose data type is one of flota16, float32, int32, int64 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.

Supported Platforms:

Ascend

Raises: TypeError – If neither x nor y is a bool Tensor.

Examples

>>> # 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.int32)
>>> 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. 0.]
 [ 0.  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.]]