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Running
on
Zero
File size: 2,139 Bytes
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# Reference: https://github.com/yxlu-0102/MP-SENet/blob/main/utils.py
import torch
import torch.nn as nn
class LearnableSigmoid1D(nn.Module):
"""
Learnable Sigmoid Activation Function for 1D inputs.
This module applies a learnable slope parameter to the sigmoid activation function.
"""
def __init__(self, in_features, beta=1):
"""
Initialize the LearnableSigmoid1D module.
Args:
- in_features (int): Number of input features.
- beta (float, optional): Scaling factor for the sigmoid function. Defaults to 1.
"""
super(LearnableSigmoid1D, self).__init__()
self.beta = beta
self.slope = nn.Parameter(torch.ones(in_features))
self.slope.requires_grad = True
def forward(self, x):
"""
Forward pass for the LearnableSigmoid1D module.
Args:
- x (torch.Tensor): Input tensor.
Returns:
- torch.Tensor: Output tensor after applying the learnable sigmoid activation.
"""
return self.beta * torch.sigmoid(self.slope * x)
class LearnableSigmoid2D(nn.Module):
"""
Learnable Sigmoid Activation Function for 2D inputs.
This module applies a learnable slope parameter to the sigmoid activation function for 2D inputs.
"""
def __init__(self, in_features, beta=1):
"""
Initialize the LearnableSigmoid2D module.
Args:
- in_features (int): Number of input features.
- beta (float, optional): Scaling factor for the sigmoid function. Defaults to 1.
"""
super(LearnableSigmoid2D, self).__init__()
self.beta = beta
self.slope = nn.Parameter(torch.ones(in_features, 1))
self.slope.requires_grad = True
def forward(self, x):
"""
Forward pass for the LearnableSigmoid2D module.
Args:
- x (torch.Tensor): Input tensor.
Returns:
- torch.Tensor: Output tensor after applying the learnable sigmoid activation.
"""
return self.beta * torch.sigmoid(self.slope * x)
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