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main_models.py
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main_models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class DCD(nn.Module):
def __init__(self,h_features=64,input_features=128):
super(DCD,self).__init__()
self.fc1=nn.Linear(input_features,h_features)
self.fc2=nn.Linear(h_features,h_features)
self.fc3=nn.Linear(h_features,4)
def forward(self,inputs):
out=F.relu(self.fc1(inputs))
out=self.fc2(out)
return F.softmax(self.fc3(out),dim=1)
class Classifier(nn.Module):
def __init__(self,input_features=64,outdim=10):
super(Classifier,self).__init__()
self.fc=nn.Linear(input_features,outdim)
def forward(self,input):
return F.softmax(self.fc(input),dim=1)
class Encoder_3c(nn.Module):
def __init__(self):
super(Encoder_3c,self).__init__()
self.conv1=nn.Conv2d(3,6,5)
self.conv2=nn.Conv2d(6,16,5)
self.fc1=nn.Linear(400,120)
self.fc2=nn.Linear(120,84)
self.fc3=nn.Linear(84,64)
def forward(self,input):
out=F.relu(self.conv1(input))
out=F.max_pool2d(out,2)
out=F.relu(self.conv2(out))
out=F.max_pool2d(out,2)
out=out.view(out.size(0),-1)
out=F.relu(self.fc1(out))
out=F.relu(self.fc2(out))
out=self.fc3(out)
return out
class Encoder_1c(nn.Module):
def __init__(self):
super(Encoder_1c,self).__init__()
self.conv1=nn.Conv2d(1,6,5)
self.conv2=nn.Conv2d(6,16,5)
self.fc1=nn.Linear(256,120)
self.fc2=nn.Linear(120,84)
self.fc3=nn.Linear(84,64)
def forward(self,input):
out=F.relu(self.conv1(input))
out=F.max_pool2d(out,2)
out=F.relu(self.conv2(out))
out=F.max_pool2d(out,2)
out=out.view(out.size(0),-1)
out=F.relu(self.fc1(out))
out=F.relu(self.fc2(out))
out=self.fc3(out)
return out