CNN executed

CNN.py

@@ -58,14 +58,14 @@ if __name__ == '__main__':
     # Hyperparameter und Konfigurationen
     params = {
         # Training
-        "epochs": [5],
+        "epochs": [20],
         "patience": [7],
         "learning_rate": [0.001],
-        "weight_decay": [5e-4] ,
+        "weight_decay": [5e-4],
         # Model
         "filter_sizes": [[2, 3, 4, 5]],
         "num_filters": [150],
-        "dropout": [0.6]
+        "dropout": [0.3]
     }
 
     # Generate permutations of hyperparameters
@@ -74,8 +74,8 @@ if __name__ == '__main__':
     best_params = {}
     best_params_rmse = -1
     # Example usage of grid_params
-    # for param_set in grid_params:
-    #     print(param_set)
+    for param_set in grid_params:
+         print(param_set)
     print('Number of grid_params:', len(grid_params))
 
     # Configs
@@ -89,7 +89,7 @@ if __name__ == '__main__':
     BATCH_SIZE = 32
 
     N_MODELS = 1
-    USE_GIRD_SEARCH = True
+    USE_GIRD_SEARCH = False
 
     models = []
     timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
@@ -114,6 +114,8 @@ if __name__ == '__main__':
 
     subset_size = len(train_dataset) // N_MODELS
     device = ml_helper.get_device(verbose=True, include_mps=False)
+    #device = torch.device("mps")
+    #print('Using device:', device)
 
     # assert if N_MODLES > 1, than grid_params should be len 1
     if N_MODELS > 1 and len(grid_params) > 1 or N_MODELS > 1 and USE_GIRD_SEARCH: