mindspore.nn.probability.bijector.Softplus

class mindspore.nn.probability.bijector.Softplus(sharpness=1.0, name='Softplus')[source]

Softplus Bijector. This Bijector performs the operation:

\[Y = \frac{\log(1 + e ^ {kX})}{k}\]

where k is the sharpness factor.

Parameters

sharpness (float, list, numpy.ndarray, Tensor) – The scale factor. Default: 1.0 .
name (str) – The name of the Bijector. Default: 'Softplus' .

Note

The dtype of sharpness must be float.

Raises: TypeError – When the dtype of the sharpness is not float.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>>
>>> # To initialize a Softplus bijector of sharpness 2.0.
>>> softplus = msb.Softplus(2.0)
>>> # To use a Softplus bijector in a network.
>>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> ans1 = softplus.forward(value)
>>> print(ans1.shape)
(3,)
>>> ans2 = softplus.inverse(value)
>>> print(ans2.shape)
(3,)
>>> ans3 = softplus.forward_log_jacobian(value)
>>> print(ans3.shape)
(3,)
>>> ans4 = softplus.inverse_log_jacobian(value)
>>> print(ans4.shape)
(3,)

property sharpness

Return the sharpness parameter of the distribution.

Returns: Tensor, the sharpness parameter of the distribution.

forward(value)

forward mapping, compute the value after mapping.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the value to compute.

forward_log_jacobian(value)

compute the log value after mapping.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the log value of forward mapping.

inverse(value)

Inverse mapping, compute the value after inverse mapping.

Parameters

value (Tensor) - the value of output after mapping.

Returns

Tensor, the value of output after mapping.

inverse_log_jacobian(value)

Compute the log value of the inverse mapping.

Parameters

value (Tensor) - the value of output after mapping.

Returns

Tensor, the log value of the inverse mapping.