Update TumorModel.py

TumorModel.py

@@ -1,37 +1,38 @@
-import torch.nn as nn
-
-class TumorClassification(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.con1d = nn.Conv2d(1, 32, 3, padding=1)
-        self.con2d = nn.Conv2d(32, 64, 3, padding=1)
-        self.con3d = nn.Conv2d(64, 128, 3, padding=1)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.fc1 = nn.Linear(128 * 28 * 28, 512)
-        self.fc2 = nn.Linear(512, 256)
-        self.output = nn.Linear(256, 4)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.pool(self.relu(self.con1d(x)))
-        x = self.pool(self.relu(self.con2d(x)))
-        x = self.pool(self.relu(self.con3d(x)))
-        x = x.view(x.size(0), -1)
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        return self.output(x)
-
-class GliomaStageModel(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(9, 100)
-        self.fc2 = nn.Linear(100, 50)
-        self.fc3 = nn.Linear(50, 30)
-        self.out = nn.Linear(30, 4)
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.relu(self.fc3(x))
-        return self.out(x)
+import torch.nn as nn
+import torch.nn.functional as F
+
+# Tumor Classification CNN
+class TumorClassification(nn.Module):
+    def __init__(self):
+        super(TumorClassification, self).__init__()
+        self.con1d = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.con2d = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.con3d = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(128 * 29 * 29, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.output = nn.Linear(256, 4)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.con1d(x)))
+        x = self.pool(F.relu(self.con2d(x)))
+        x = self.pool(F.relu(self.con3d(x)))
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.output(x)
+
+# Glioma Stage Predictor
+class GliomaStageModel(nn.Module):
+    def __init__(self):
+        super(GliomaStageModel, self).__init__()
+        self.fc1 = nn.Linear(9, 100)
+        self.fc2 = nn.Linear(100, 50)
+        self.fc3 = nn.Linear(50, 30)
+        self.out = nn.Linear(30, 4)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.relu(self.fc3(x))
+        return self.out(x)