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