mindspore.nn.LBeta

class mindspore.nn.LBeta[source]

This is semantically equal to

\[P(x, y) = lgamma(x) + lgamma(y) - lgamma(x + y).\]

The method is more accurate for arguments above 8. The reason for accuracy loss in the naive computation is catastrophic cancellation between the lgammas. This method avoids the numeric cancellation by explicitly decomposing lgamma into the Stirling approximation and an explicit log_gamma_correction, and cancelling the large terms from the Striling analytically.

Inputs:

x (Tensor) - The input tensor. With float16 or float32 data type. x should have the same dtype with y.
y (Tensor) - The input tensor. With float16 or float32 data type. y should have the same dtype with x.

Outputs:

Tensor, has the same dtype as x and y.

Raises: TypeError – If dtype of x or y is neither float16 nor float32, or if x has different dtype with y.

Supported Platforms:: Ascend GPU

Examples

>>> input_x = Tensor(np.array([2.0, 4.0, 6.0, 8.0]).astype(np.float32))
>>> input_y = Tensor(np.array([2.0, 3.0, 14.0, 15.0]).astype(np.float32))
>>> lbeta = nn.LBeta()
>>> output = lbeta(input_y, input_x)
>>> print(output)
[-1.7917596  -4.094345  -12.000229  -14.754799]