diff --git a/CNN-model/data_utils.py b/CNN-model/data_utils.py
index 19df137..a005338 100644
--- a/CNN-model/data_utils.py
+++ b/CNN-model/data_utils.py
@@ -3,7 +3,7 @@ from torch.utils.data import TensorDataset
 
 
 def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
-    data = torch.tensor(data, dtype=torch.uint8)
+    data = torch.tensor(list(data), dtype=torch.uint8)
     sample_count = data.shape[0] - context_length
     x = data.unfold(0, context_length, 1)[:sample_count]
     y = data[context_length:]
diff --git a/CNN-model/main_cnn.py b/CNN-model/main_cnn.py
index 98cbaa0..bcdc5d7 100644
--- a/CNN-model/main_cnn.py
+++ b/CNN-model/main_cnn.py
@@ -1,11 +1,13 @@
 import torch
 import torch.nn as nn
-import torch.functional as F
+import torch.nn.functional as F
 import optuna.trial as tr
 from torch.utils.data import DataLoader
+from tqdm import tqdm
 
 from optuna_trial import create_model
 from data_utils import make_context_pairs
+import optuna
 
 # hyper parameters
 context_length = 128
@@ -18,18 +20,61 @@ def train_and_eval(
         epochs: int = 100,
         learning_rate: float = 1e-3,
         device: torch.device = torch.device("cpu")
-):
+) -> dict:
     model.to(device)
     optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-    training_loader = DataLoader(make_context_pairs(training_data, context_length=context_length))
-    validation_loader= DataLoader(make_context_pairs(validation_data, context_length=context_length))
+    training_loader = DataLoader(make_context_pairs(training_data, context_length=context_length), batch_size=batch_size)
+    validation_loader= DataLoader(make_context_pairs(validation_data, context_length=context_length), batch_size=batch_size)
+
+    training_losses = []
+    validation_losses = []
+    best_val_loss = float("inf")
 
     for epoch in range(epochs):
         model.train()
+        train_loss = 0
+        for x, y in tqdm(training_loader, desc=f"Epoch {epoch}"):
+            x, y = x.to(device), y.to(device)
+            prediction = model(x)
+            loss = F.cross_entropy(prediction, y)
+            train_loss += loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        training_losses.append(train_loss / len(training_loader))
+
+        model.eval()
+
+        with torch.no_grad():
+            val_loss = 0
+            for x, y in validation_loader:
+                x, y = x.to(device), y.to(device)
+                prediction = model(x)
+                loss = F.cross_entropy(prediction, y)
+                val_loss += loss.item()
+            validation_losses.append(val_loss / len(validation_loader))
+            if validation_losses[-1] < best_val_loss:
+                best_val_loss = validation_losses[-1]
+
+    return {
+        "training_losses": training_losses,
+        "validation_losses": validation_losses,
+        "best_validation_loss": best_val_loss
+    }
 
 
-def objective_function(trial: tr.Trial):
+
+def objective_function(trial: tr.Trial, train_data: bytes, validation_data: bytes, batch_size: int):
     model = create_model(trial)
+    result = train_and_eval(model, train_data, validation_data, batch_size)
+    return result["best_validation_loss"]
 
 if __name__ == "__main__":
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    train_data = b""
+    validation_data = b""
+    batch_size = 0
+
+    study = optuna.create_study(study_name="CNN network",direction="minimize")
+    study.optimize(lambda trial: objective_function(trial, train_data, validation_data, batch_size), n_trials=10)
\ No newline at end of file