new fhe compilations

v2/client.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a31c3726f23039230c9b7b8967cd09138da91576e6a61d6ec708bcc9c6734cf
+size 10773

v2/model.py

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+class SeizureDetectionCNN(nn.Module):
+    def init(self, num_classes=2):
+        super(SeizureDetectionCNN, self).init()
+        self.conv1= nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1) # 32, 32, 32
+
+        self.pool= nn.MaxPool2d(kernel_size=2, stride=2) # 32, 16, 16
+
+        self.conv2= nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) # 64, 16, 16 -> 64, 8, 8
+
+        # Adding Batch Normalization
+        self.bn1 = nn.BatchNorm2d(32)
+        self.bn2 = nn.BatchNorm2d(64)
+
+        self.dropout = nn.Dropout(p=0.5)  # Dropout with a probability of 50%
+
+        self.fc1= nn.Linear(64 * 8 * 8, 120)
+        self.fc2= nn.Linear(120, 32)
+        self.fc3= nn.Linear(32, num_classes)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.bn1(self.conv1(x))))  # 32, 32, 32
+        x = self.pool(F.relu(self.bn2(self.conv2(x))))  # 64, 8, 8
+
+        x = torch.flatten(x, 1)
+        x = self.dropout(F.relu(self.fc1(x)))  # Apply dropout
+        x = self.dropout(F.relu(self.fc2(x)))  # Apply dropout
+        x = self.fc3(x)
+        return x

v2/server.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1c7dd6ca3a84d769fe89f2d5ea1bbd95376bc803dd2a1230a0bc379aed134d4
+size 244437