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45 lines
1.2 KiB
45 lines
1.2 KiB
2 years ago
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class ANN_Model(nn.Module):
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def __init__(self,input_features=8,out_features=72):
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super().__init__()
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self.fc1=nn.Linear(input_features,12)
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self.fc2=nn.Linear(12,16)
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self.fc3=nn.Linear(16,20)
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self.fc4=nn.Linear(20,25)
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self.fc5=nn.Linear(50,60)
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self.fc6=nn.Linear(60,70)
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self.fc7=nn.Linear(70,80)
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self.fc8=nn.Linear(80,90)
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self.fc9=nn.Linear(90,100)
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self.fc10=nn.Linear(100,90)
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self.fc11=nn.Linear(90,80)
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self.out=nn.Linear(80,out_features)
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def forward(self,x):
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density=x[:,:25].reshape(x.shape[0],25)
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displace = x[:,25:]
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x = F.relu(self.fc1(displace))
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x = F.relu(self.fc2(x))
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x = F.relu(self.fc3(x))
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x = F.relu(self.fc4(x))
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x = torch.hstack((density,x))
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x = F.relu(self.fc5(x))
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x = F.relu(self.fc6(x))
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x = F.relu(self.fc7(x))
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x = F.relu(self.fc8(x))
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x = F.relu(self.fc9(x))
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x = F.relu(self.fc10(x))
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x = F.relu(self.fc11(x))
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x = self.out(x)
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return x
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