8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import torch
|
|
from torch.nn.parameter import Parameter
|
|
|
|
|
|
class AdaptiveSoftplus(torch.nn.Module):
|
|
'''
|
|
Implementation of soft exponential activation.
|
|
Shape:
|
|
- Input: (N, *) where * means, any number of additional
|
|
dimensions
|
|
- Output: (N, *), same shape as the input
|
|
Parameters:
|
|
- alpha - trainable parameter
|
|
References:
|
|
- See related paper:
|
|
https://arxiv.org/pdf/1602.01321.pdf
|
|
Examples:
|
|
>>> a1 = soft_exponential(256)
|
|
>>> x = torch.randn(256)
|
|
>>> x = a1(x)
|
|
'''
|
|
|
|
def __init__(self):
|
|
'''
|
|
Initialization.
|
|
INPUT:
|
|
- in_features: shape of the input
|
|
- aplha: trainable parameter
|
|
aplha is initialized with zero value by default
|
|
'''
|
|
super().__init__()
|
|
|
|
self.soft = torch.nn.Softplus()
|
|
|
|
self.scale = Parameter(torch.rand(1))
|
|
self.scale.requiresGrad = True # set requiresGrad to true!
|
|
|
|
def forward(self, x):
|
|
'''
|
|
Forward pass of the function.
|
|
Applies the function to the input elementwise.
|
|
'''
|
|
#x += self.translate
|
|
return self.soft(x) * self.scale
|