subset update+plots

cnn_bootstrap_agg.py

@@ -42,7 +42,7 @@ def train_model(model, train_dataset, val_dataset, criterion, optimizer, epochs,
         history['train_loss'].append(train_loss)
         history['train_r2'].append(train_r2)
 
-        # **Validierung nach jeder Epoche**
+        
         model.eval()
         val_loss = 0
         all_val_preds, all_val_targets = [], []
@@ -64,12 +64,12 @@ def train_model(model, train_dataset, val_dataset, criterion, optimizer, epochs,
 
         print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Train R²: {train_r2:.4f}, Val R²: {val_r2:.4f}")
 
-    return history  # **Gibt die Verlaufsdaten zurück**
+    return history 
 
 
 def bootstrap_aggregation(ModelClass, train_dataset, num_models=3, epochs=5, batch_size=32, learning_rate=0.001):
     models = []
-    all_histories = []  # **Speichert Trainingsverlauf aller Modelle**
+    all_histories = []  
 
     subset_size = len(train_dataset) // num_models
 
@@ -82,7 +82,7 @@ def bootstrap_aggregation(ModelClass, train_dataset, num_models=3, epochs=5, bat
 
         subset = Subset(train_dataset, subset_indices)
 
-        # **Validierungsdaten als restliche Daten**
+        
         val_indices = list(range(start_idx, end_idx))
         val_subset = Subset(train_dataset, val_indices)
 
@@ -91,7 +91,7 @@ def bootstrap_aggregation(ModelClass, train_dataset, num_models=3, epochs=5, bat
         optimizer = optim.Adam(model.parameters(), lr=learning_rate)
 
         history = train_model(model, subset, val_subset, criterion, optimizer, epochs, batch_size)
-        all_histories.append(history)  # **Speichere Verlaufsdaten**
+        all_histories.append(history)  
         models.append(model)
 
     return models, all_histories
@@ -116,7 +116,7 @@ def plot_training_histories(histories, num_models):
 
     fig, axes = plt.subplots(1, 2, figsize=(14, 5))
 
-    # **Links: Trainings- und Validierungsverlust**
+   
     for i in range(num_models):
         axes[0].plot(epochs, histories[i]['train_loss'], label=f"Train Loss Model {i+1}")
         axes[0].plot(epochs, histories[i]['val_loss'], linestyle='dashed', label=f"Val Loss Model {i+1}")
@@ -126,7 +126,6 @@ def plot_training_histories(histories, num_models):
     axes[0].set_ylabel("Loss")
     axes[0].legend()
 
-    # **Rechts: R²-Werte für Training und Validierung**
     for i in range(num_models):
         axes[1].plot(epochs, histories[i]['train_r2'], label=f"Train R² Model {i+1}")
         axes[1].plot(epochs, histories[i]['val_r2'], linestyle='dashed', label=f"Val R² Model {i+1}")