feat: 0.39 on local

cs2109s/labs/final/final.py

@@ -23,37 +23,29 @@ class CNN3D(nn.Module):
         super(CNN3D, self).__init__()
         self.conv1 = nn.Conv3d(1, 16, 2, 1, 2)
         self.batchnorm3d = nn.BatchNorm3d(16)
-        self.batchnorm1d = nn.BatchNorm1d(64)
 
         self.dropout = nn.Dropout(0.5)
         self.mp3d = nn.AvgPool3d(2)
-        self.relu = nn.ReLU()
+        self.relu = nn.LeakyReLU()
 
         self.lstm = nn.LSTM(5184, 64, 1, batch_first=True)
         self.fc2 = nn.Linear(64, 6)
 
     def forward(self, x):
         x = self.conv1(x)
-        x = self.mp3d(x)
-        x = self.batchnorm3d(x)
         x = self.relu(x)
+        x = self.batchnorm3d(x)
+        x = self.mp3d(x)
         x = self.dropout(x)
 
         x = x.view(-1, 5184)
 
-        # print(x.shape)
-
         x, _ = self.lstm(x)
-        # print(x.shape)
-        x = self.batchnorm1d(x)
         x = self.relu(x)
-        x = self.dropout(x)
-
         x = self.fc2(x)
         return torch.softmax(x, dim=1)
 
-
-def train(model, criterion, optimizer, loader, epochs=20):
+def train(model, criterion, optimizer, loader, epochs=10):
     for epoch in range(epochs):
         for idx, (inputs, labels) in enumerate(loader):
             optimizer.zero_grad()
@@ -82,7 +74,7 @@ def process_data(X, y):
     # Get the indices of each class
     indices = [np.argwhere(y == i).squeeze(1) for i in range(6)]
     # Get the number of samples to take for each class
-    num_samples_to_take = 300
+    num_samples_to_take = 1500
     # Get the indices of the samples to take
     indices_to_take = [np.random.choice(indices[i], num_samples_to_take, replace=True) for i in range(6)]
     # Concatenate the indices
@@ -102,8 +94,8 @@ class Model():
     def fit(self, X, y):
         X, y = process_data(X, y)
         train_dataset = torch.utils.data.TensorDataset(X, y)
-        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
-        train(self.model, self.criterion, self.optimizer, train_loader, 10)
+        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
+        train(self.model, self.criterion, self.optimizer, train_loader, 5)
 
     def predict(self, X):
         self.model.eval()