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 input is zero.
\[out_i = input_{i}\ln{other_{i}}\]Inputs of input and other comply with the implicit type conversion rules to make the data types consistent.
- Inputs:
- Outputs:
y (Tensor) - the shape is the broadcast of input and other, and the data type is the one with higher precision or higher digits among the two inputs.
- Raises
TypeError – If input is not a Tensor, number or bool.
TypeError – If other is not a Tensor, number or bool.
ValueError – If input and other can not broadcast.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> input = Tensor(np.array([-5, 0, 4]), mindspore.float32) >>> other = Tensor(np.array([2, 2, 2]), mindspore.float32) >>> op = ops.Xlogy() >>> output = op(input, other) >>> print(output) [-3.465736 0. 2.7725887]