mindspore.ops.Xlogy

class mindspore.ops.Xlogy[source]

Computes the first input tensor multiplied by the logarithm of second input tensor element-wise. Returns zero when x is zero.

Refer to mindspore.ops.xlogy() for more details.

Inputs:

x (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_.
y (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.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-5, 0, 4]), mindspore.float32)
>>> y = Tensor(np.array([2, 2, 2]), mindspore.float32)
>>> xlogy = ops.Xlogy()
>>> output = xlogy(x, y)
>>> print(output)
[-3.465736   0.        2.7725887]