feat: uhm, i changed some things

CNN-model/trainers/OptunaTrainer.py

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+from typing import Callable
+
+import optuna
+import optuna.trial as tr
+import torch
+from torch import nn as nn
+from torch.utils.data import DataLoader
+
+from trainer import Trainer
+from ..model.cnn import CNNPredictor
+from train import train
+
+
+def create_model(trial: tr.Trial, vocab_size: int = 256):
+    num_layers = trial.suggest_int("num_layers", 1, 6)
+    hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
+    kernel_size = trial.suggest_int("kernel_size", 2, 7)
+    dropout_prob = trial.suggest_float("dropout_prob", 0.1, 0.5)
+    use_batchnorm = trial.suggest_categorical("use_batchnorm", [True, False])
+
+    return CNNPredictor(
+        vocab_size=vocab_size,
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        kernel_size=kernel_size,
+        dropout_prob=dropout_prob,
+        use_batchnorm=use_batchnorm
+    )
+
+
+def objective_function(
+        trial: tr.Trial,
+        training_loader: DataLoader,
+        validation_loader: DataLoader,
+        loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+        device: str
+):
+    model = create_model(trial).to(device)
+    _, validation_loss = train(model, training_loader, validation_loader, loss_fn)
+    return min(validation_loss)
+
+
+class OptunaTrainer(Trainer):
+    def execute(
+            self,
+            model: nn.Module | None,
+            train_loader: DataLoader,
+            validation_loader: DataLoader,
+            loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+            n_epochs: int,
+            device: str
+    ) -> None:
+        study = optuna.create_study(study_name="CNN network", direction="minimize")
+        study.optimize(
+            lambda trial: objective_function(trial, train_loader, validation_loader, loss_fn, device),
+            n_trials=20
+        )
+
+        best_params = study.best_trial.params
+        best_model = CNNPredictor(
+            **best_params
+        )
+        torch.save(best_model, "models/final_model.pt")