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:
AscendGPUCPU
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.