| """ |
| Script based on: |
| Wang, Xueliang, Honge Ren, and Achuan Wang. |
| "Smish: A Novel Activation Function for Deep Learning Methods. |
| " Electronics 11.4 (2022): 540. |
| smish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + sigmoid(x))) |
| """ |
|
|
| |
| |
| from torch import nn |
|
|
| from .Fsmish import smish |
|
|
|
|
| class Smish(nn.Module): |
| """ |
| Applies the mish function element-wise: |
| mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) |
| Shape: |
| - Input: (N, *) where * means, any number of additional |
| dimensions |
| - Output: (N, *), same shape as the input |
| Examples: |
| >>> m = Mish() |
| >>> input = torch.randn(2) |
| >>> output = m(input) |
| Reference: https://pytorch.org/docs/stable/generated/torch.nn.Mish.html |
| """ |
|
|
| def __init__(self): |
| """ |
| Init method. |
| """ |
| super().__init__() |
|
|
| def forward(self, input): |
| """ |
| Forward pass of the function. |
| """ |
| return smish(input) |
|
|
