feat: optuna optimization performed

CNN-model/model/cnn.py

@@ -6,7 +6,7 @@ class CNNPredictor(nn.Module):
             self, 
             vocab_size=256, 
             embed_dim=64, 
-            hidden_channels=128,
+            hidden_dim=128,
         ):
         super().__init__()
 
@@ -15,11 +15,11 @@ class CNNPredictor(nn.Module):
 
         # 2. Convolutional feature extractor
         self.conv_layers = nn.Sequential(
-            nn.Conv1d(embed_dim, hidden_channels, kernel_size=5, padding=2),
+            nn.Conv1d(embed_dim, hidden_dim, kernel_size=5, padding=2),
             nn.ReLU(),
-            nn.Conv1d(hidden_channels, hidden_channels, kernel_size=5, padding=2),
+            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
             nn.ReLU(),
-            nn.Conv1d(hidden_channels, hidden_channels, kernel_size=5, padding=2),
+            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
             nn.ReLU(),
         )
 
@@ -27,7 +27,7 @@ class CNNPredictor(nn.Module):
         self.pool = nn.AdaptiveAvgPool1d(1)  # → (B, hidden_channels, 1)
 
         # 4. Final classifier
-        self.fc = nn.Linear(hidden_channels, vocab_size)  # → (B, 256)
+        self.fc = nn.Linear(hidden_dim, vocab_size)  # → (B, 256)
 
     def forward(self, x):
         """