feat: optuna optimization performed

CNN-model/dataset_loaders/LoremIpsumDataset.py

@@ -17,19 +17,18 @@ class LoremIpsumDataset(Dataset):
         path = join(curdir, "data")
         self._root = path
         # Convert text to bytes (UTF-8 encoded)
-        self.dataset = torch.tensor([ord(c) for c in list(_text)], dtype=torch.long)
-
-        sequence_count = self.dataset.shape[0] // 128 # how many vectors of 128 elements can we make
-        self.dataset = self.dataset[:sequence_count * 128]
-        self.dataset = self.dataset.view(-1, 128)
-
-        print(self.dataset.shape)
+        self.dataset = torch.tensor([ord(c) % 256 for c in list(_text)], dtype=torch.long)
+        self.context_length = 128
 
     def __len__(self):
         # Number of possible sequences of length sequence_length
-        return self.dataset.size(0)
+        return self.dataset.size(0) - self.context_length
 
     def __getitem__(self, idx):
+        x = self.dataset[idx: idx + self.context_length]
+        y = self.dataset[idx + self.context_length]
+
         if self.transform is not None:
-            return self.transform(self.dataset[idx])
-        return self.dataset[idx]
+            x = self.transform(x)
+
+        return x, y

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):
         """

CNN-model/trainers/OptunaTrainer.py

@@ -13,7 +13,7 @@ from .train import train
 
 def create_model(trial: tr.Trial, vocab_size: int = 256):
     hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
-    embedding_dim = trial.suggest_int("embedding_dim", 64, 512, log=True)
+    embedding_dim = trial.suggest_int("embed_dim", 64, 512, log=True)
 
     return CNNPredictor(
         vocab_size=vocab_size,

CNN-model/trainers/train.py

@@ -56,6 +56,4 @@ def train(
             avg_loss = sum(losses) / len(losses)
             avg_validation_losses.append(avg_loss)
 
-        tqdm.write(f"epoch: {epoch + 1}, avg val loss = {avg_loss:.4f}")
-
     return avg_training_losses, avg_validation_losses