diff --git a/Pilot2/P2B1/p2b1.py b/Pilot2/P2B1/p2b1.py
index 66d69562..46634b47 100755
--- a/Pilot2/P2B1/p2b1.py
+++ b/Pilot2/P2B1/p2b1.py
@@ -254,6 +254,7 @@ def datagen(self, epoch=0, print_out=1, test=0):
             if print_out:
                 print (files[f_ind], '\n')
 
+            print (files[f_ind], '\n')
             (X, nbrs, resnums) = helper.get_data_arrays(files[f_ind])
 
             # normalizing the location coordinates and bond lengths and scale type encoding
@@ -269,8 +270,13 @@ def datagen(self, epoch=0, print_out=1, test=0):
 
             xt_all = np.array([])
             yt_all = np.array([])
+            frames_all = np.array([])
 
-            for i in range(num_frames):
+            num_active_frames = random.sample(range(num_frames), 
+                                              int(self.sampling_density*num_frames))
+            print ('Formating on the following frames', self.num_active_frames)
+            print ('Datagen on the following frames', num_active_frames)
+            for i in num_active_frames:
 
                 if self.conv_net:
                     xt = Xnorm[i]
@@ -325,7 +331,10 @@ def train_ac(self):
             encoder_weight_file = '%s/%s.hdf5' % (current_path, 'encoder_weights')
 
             for curr_file, xt_all, yt_all in self.datagen(i):
-                for frame in random.sample(range(len(xt_all)), int(self.sampling_density*len(xt_all))):
+                print ('Training on the following frames', xt_all)
+                for frame in self.num_active_frames:
+#                for frame in random.sample(range(len(xt_all)), int(self.sampling_density*len(xt_all))):
+#                for frame in range(xt_all):
 
                     history = self.molecular_model.fit(xt_all[frame], yt_all[frame], epochs=1,
                                                        batch_size=self.batch_size, callbacks=self.callbacks[:2],