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.

o u t_{i} = i n p u t_{i} \ln o t h e r_{i}

Inputs of input and other comply with the implicit type conversion rules to make the data types consistent.

Inputs:

input (Tensor, numbers.Number, bool) - The first input is a numbers.Number or a bool or a tensor whose data type is number or bool_.
other (Tensor, numbers.Number, bool) - The second input is a numbers.Number or a bool or a tensor whose data type is number or bool_.

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]