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CNN for KT transition in 2DXY model/net.py

@@ -2,15 +2,19 @@
 import torch
 from torch import nn
 
-class xynn(nn.Module):
+class xy(nn.Module):
+
     def __init__(self, lattice):
+        super(xy, self).__init__()
         self.lattice = lattice
         self.c1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3)
         self.r1 = nn.ReLU(True)
         self.m1 = nn.MaxPool2d(kernel_size=(2,2))
-        self.c2 = nn.Conv2d(in_channels=8, out_channels=16)
+        self.c2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3)
         self.r2 = nn.ReLU(True)
-        self.f1 = nn.Linear()
+        self.m2 = nn.MaxPool2d(kernel_size=(2, 2))
+        self.l1 = nn.Linear(576, 32)
+        self.l2 = nn.Linear(32, 1)
         self.s = nn.Sigmoid()
 
     def forward(self, spin_data):
@@ -19,7 +23,48 @@ def forward(self, spin_data):
         x = self.m1(x)
         x = self.c2(x)
         x = self.r2(x)
-        x = self.f1(x)
+        x = self.m2(x)
+        x = x.view(x.shape[0], -1)
+        x = self.l1(x)
+        x = self.l2(x)
         x = self.s(x)
 
-        return x
+        return x
+
+
+class LeNet(nn.Module):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.C1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
+        self.R1 = nn.ReLU()
+        self.S2 = nn.MaxPool2d(kernel_size=2)
+        self.C3 = nn.Conv2d(6, 16, 5, 1, 0)
+        self.R2 = nn.ReLU()
+        self.S4 = nn.MaxPool2d(2)
+        self.C5 = nn.Conv2d(16, 120, 5, 1, 0)
+        self.R3 = nn.ReLU()
+        self.F6 = nn.Linear(in_features=120, out_features=84)
+        self.R4 = nn.ReLU()
+        self.OUT = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.C1(x)
+        x = self.R1(x)
+        x = self.S2(x)
+        x = self.C3(x)
+        x = self.R2(x)
+        x = self.S4(x)
+        x = self.C5(x)
+        x = self.R3(x)
+        x = x.view(x.size(0), -1)
+        x = self.F6(x)
+        x = self.R4(x)
+        x = self.OUT(x)
+        return x
+
+
+if __name__ == "__main__":
+    model = xy(32)
+    a = torch.randn(1, 1, 32, 32)
+    b = model(a)
+    print(b.shape)