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]