diff --git a/video_prediction_tools/utils/runscript_generator/config_postprocess.py b/video_prediction_tools/utils/runscript_generator/config_postprocess.py
index eff2694bbd1dc381726e54bb4fb46bf0af5ddb4c..544337834fd4a9a1241f8e20e2cf186d8a2265ce 100755
--- a/video_prediction_tools/utils/runscript_generator/config_postprocess.py
+++ b/video_prediction_tools/utils/runscript_generator/config_postprocess.py
@@ -76,24 +76,32 @@ class Config_Postprocess(Config_runscript_base):
         self.model = os.path.basename(dir_base)
         # List the subdirectories...
         _ = Config_Postprocess.get_subdir_list(dir_base)
-        # ... and obtain the checkpoint directory
+        
+        # Chose the checkpoint directory
+        ckp_req_str = "Chose a checkpoint directory from the list above:"
+        ckp_req_err = NotADirectoryError("Could not find the passed directory.")
+        dir_base = Config_Postprocess.keyboard_interaction(ckp_req_str, Config_Postprocess.check_dir, ckp_req_err,
+                                                           prefix2arg=dir_base+"/", ntries=2)
+         # List the subdirectories...
+        _ = Config_Postprocess.get_subdir_list(dir_base) 
+        # ... and obtain the model directory with checkpoints
         trained_dir_req_str = "Choose a trained model from the experiment list above:"
         trained_err = FileNotFoundError("No trained model parameters found.")
-
         self.checkpoint_dir = Config_Postprocess.keyboard_interaction(trained_dir_req_str,
                                                                       Config_Postprocess.check_traindir,
                                                                       trained_err, ntries=3, prefix2arg=dir_base+"/")
 
+        
         # get the relevant information from checkpoint_dir in order to construct source_dir and results_dir
         # (following naming convention)
         cp_dir_split = Config_Postprocess.path_rec_split(self.checkpoint_dir)
         cp_dir_split = list(filter(None, cp_dir_split))                       # get rid of empty list elements
 
-        base_dir, exp_dir_base, exp_dir = "/"+os.path.join(*cp_dir_split[:-4]), cp_dir_split[-3], cp_dir_split[-1]
+        base_dir, exp_dir_base, exp_dir = "/"+os.path.join(*cp_dir_split[:-4]), cp_dir_split[-3], cp_dir_split[-2]
         self.runscript_target = self.rscrpt_tmpl_prefix + self.dataset + "_" + exp_dir + ".sh"
 
         # Set results_dir
-        self.results_dir = os.path.join(base_dir, "results", exp_dir_base, self.model, exp_dir)
+        self.results_dir = os.path.join(base_dir, "results", exp_dir_base,self.model, exp_dir)
 
         return
         # Decide if quick evaluation should be performed