Update TumorModel.py

TumorModel.py

@@ -2,49 +2,39 @@ import torch.nn as nn
 
 class TumorClassification(nn.Module):
     def __init__(self):
-        super(TumorClassification, self).__init__()
-        self.con1d = nn.Conv2d(1, 32, kernel_size=3, padding=1)
-        self.relu1 = nn.ReLU()
-        self.pool1 = nn.MaxPool2d(2)
-
-        self.con2d = nn.Conv2d(32, 64, kernel_size=3, padding=1)
-        self.relu2 = nn.ReLU()
-        self.pool2 = nn.MaxPool2d(2)
-
-        self.con3d = nn.Conv2d(64, 128, kernel_size=3, padding=1)
-        self.relu3 = nn.ReLU()
-        self.pool3 = nn.MaxPool2d(2)
-
-        self.flatten = nn.Flatten()
-        self.fc1 = nn.Linear(128 * 28 * 28, 512)
-        self.relu4 = nn.ReLU()
-        self.fc2 = nn.Linear(512, 256)
-        self.relu5 = nn.ReLU()
-        self.output = nn.Linear(256, 4)
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Conv2d(1, 32, 3, 1, 1),      # con1d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(32, 64, 3, 1, 1),     # con2d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(64, 128, 3, 1, 1),    # con3d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Flatten(),
+            nn.Linear(128 * 26 * 26, 512),  # 128×26×26 = 86528
+            nn.ReLU(),
+            nn.Linear(512, 4)               # 4 classes
+        )
 
     def forward(self, x):
-        x = self.pool1(self.relu1(self.con1d(x)))
-        x = self.pool2(self.relu2(self.con2d(x)))
-        x = self.pool3(self.relu3(self.con3d(x)))
-        x = self.flatten(x)
-        x = self.relu4(self.fc1(x))
-        x = self.relu5(self.fc2(x))
-        return self.output(x)
-
+        return self.model(x)
 
 class GliomaStageModel(nn.Module):
     def __init__(self):
-        super(GliomaStageModel, self).__init__()
+        super().__init__()
         self.fc1 = nn.Linear(9, 100)
-        self.relu1 = nn.ReLU()
         self.fc2 = nn.Linear(100, 50)
-        self.relu2 = nn.ReLU()
         self.fc3 = nn.Linear(50, 30)
-        self.relu3 = nn.ReLU()
-        self.out = nn.Linear(30, 2)
+        self.out = nn.Linear(30, 4)  # 4 stages
 
     def forward(self, x):
-        x = self.relu1(self.fc1(x))
-        x = self.relu2(self.fc2(x))
-        x = self.relu3(self.fc3(x))
+        x = nn.functional.relu(self.fc1(x))
+        x = nn.functional.relu(self.fc2(x))
+        x = nn.functional.relu(self.fc3(x))
         return self.out(x)