diff --git a/learn_it.py b/learn_it.py
index a06d4db..b01d345 100644
--- a/learn_it.py
+++ b/learn_it.py
@@ -41,6 +41,7 @@ import time
 import dataconf
 import logging
 from datetime import datetime
+import glob
 
 from Dataset import (
     DatasetMaster,
@@ -220,26 +221,43 @@ for id in range(0, len(network)):
             + ".npy"
         )
 
-    # Are there weights that overwrite the initial weights?
-    file_to_load: str = (
-        cfg.learning_parameters.overload_path + "/Weight_L" + str(id) + ".npy"
-    )
-    if os.path.exists(file_to_load) is True:
-        network[id].weights = torch.tensor(
-            np.load(file_to_load),
-            dtype=torch.float64,
-        )
-        wf[id] = np.load(file_to_load)
-        logging.info(f"File used: {file_to_load}")
+for id in range(0, len(network)):
 
-    file_to_load = cfg.learning_parameters.overload_path + "/EpsXY_L" + str(id) + ".npy"
-    if os.path.exists(file_to_load) is True:
-        network[id].epsilon_xy = torch.tensor(
-            np.load(file_to_load),
+    # Are there weights that overwrite the initial weights?
+    file_to_load = glob.glob(
+        cfg.learning_parameters.overload_path + "/Weight_L" + str(id) + "*.npy"
+    )
+
+    if len(file_to_load) > 1:
+        raise Exception(
+            f"Too many previous weights files {cfg.learning_parameters.overload_path}/Weight_L{id}*.npy"
+        )
+
+    if len(file_to_load) == 1:
+        network[id].weights = torch.tensor(
+            np.load(file_to_load[0]),
             dtype=torch.float64,
         )
-        eps_xy[id] = np.load(file_to_load)
-        logging.info(f"File used: {file_to_load}")
+        wf[id] = np.load(file_to_load[0])
+        logging.info(f"File used: {file_to_load[0]}")
+
+    # Are there epsinlon xy files that overwrite the initial epsilon xy?
+    file_to_load = glob.glob(
+        cfg.learning_parameters.overload_path + "/EpsXY_L" + str(id) + "*.npy"
+    )
+
+    if len(file_to_load) > 1:
+        raise Exception(
+            f"Too many previous epsilon xy files {cfg.learning_parameters.overload_path}/EpsXY_L{id}*.npy"
+        )
+
+    if len(file_to_load) == 1:
+        network[id].epsilon_xy = torch.tensor(
+            np.load(file_to_load[0]),
+            dtype=torch.float64,
+        )
+        eps_xy[id] = np.load(file_to_load[0])
+        logging.info(f"File used: {file_to_load[0]}")
 
 #######################################################################
 # Optimizer and LR Scheduler                                          #