diff --git a/apps/trainers/FaceAligner/FaceAlignerTrainerApp.py b/apps/trainers/FaceAligner/FaceAlignerTrainerApp.py
index da70167..e045021 100644
--- a/apps/trainers/FaceAligner/FaceAlignerTrainerApp.py
+++ b/apps/trainers/FaceAligner/FaceAlignerTrainerApp.py
@@ -369,9 +369,11 @@ class FaceAlignerTrainerApp:
             d = len(lh) // max_lines
 
             lh_ar = np.array(lh[-d*max_lines:], np.float32)
-            lh_ar = lh_ar.reshape( (max_lines, d)).mean(-1)
-
-            print( '\n'.join( f'{value:.5f}' for value in lh_ar ) )
+            lh_ar = lh_ar.reshape( (max_lines, d))
+            lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median = lh_ar.max(-1), lh_ar.min(-1), lh_ar.mean(-1), np.median(lh_ar, -1)
+            
+            
+            print( '\n'.join( f'max:[{max_value:.5f}] min:[{min_value:.5f}] mean:[{mean_value:.5f}] median:[{median_value:.5f}]' for max_value, min_value, mean_value, median_value in zip(lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median) ) )
 
         dlg.recreate().set_current()
 
diff --git a/xlib/torch/optim/AdaBelief.py b/xlib/torch/optim/AdaBelief.py
index aedc91f..ee06716 100644
--- a/xlib/torch/optim/AdaBelief.py
+++ b/xlib/torch/optim/AdaBelief.py
@@ -1,5 +1,4 @@
 import torch
-import numpy as np
 
 class AdaBelief(torch.optim.Optimizer):
     def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
@@ -39,21 +38,15 @@ class AdaBelief(torch.optim.Optimizer):
                     grad.add_(p.data, alpha=group['weight_decay'])
 
                 state['step'] += 1
-                
+
                 m_t, v_t = state['m_t'], state['v_t']
                 m_t.mul_(beta1).add_( grad            , alpha=1 - beta1)
                 v_t.mul_(beta2).add_( (grad - m_t)**2 , alpha=1 - beta2)
-                
+
                 v_diff = (-group['lr'] * m_t).div_( v_t.sqrt().add_(group['eps']) )
-                
+
                 if group['lr_dropout'] < 1.0:
                     lrd_rand = torch.ones_like(p.data)
                     v_diff *= torch.bernoulli(lrd_rand * group['lr_dropout'] )
-                    
-                    # from xlib.console.diacon import Diacon
-                    # Diacon.stop()
-                    # import code
-                    # code.interact(local=dict(globals(), **locals()))
-                    
 
                 p.data.add_(v_diff)