mindspore.ops.Xlogy

class mindspore.ops.Xlogy

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

\[out_i = x_{i}\ln{y_{i}}\]

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, dtypes of them cannot be both bool, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

Inputs:

• x (Union[Tensor, Number, bool]) - The first input is a number or a bool or a tensor whose data type is float16, float32 or bool.

• y (Union[Tensor, Number, bool]) - The second input is a number or a bool when the first input is a tensor or a tensor whose data type is float16, float32 or bool. The value must be positive.

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.

Raises

TypeError – If x and y is not one of the following: Tensor, Number, bool.

Supported Platforms:

Ascend

Examples

>>> 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]