diff --git a/models/Model_SAE/Model.py b/models/Model_SAE/Model.py
index 752bb33..14fba1a 100644
--- a/models/Model_SAE/Model.py
+++ b/models/Model_SAE/Model.py
@@ -317,7 +317,7 @@ class SAEModel(ModelBase):
         #    x = np.array (self.src_sample_losses)
         #    self.src_sample_losses = []
         #    b = x[:,1]
-        #    idxs = (x[:,0][ np.argwhere ( b [ b > np.mean(b) ] )[:,0] ]).astype(np.uint)
+        #    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
         #    generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
         #    
         #    
@@ -326,7 +326,7 @@ class SAEModel(ModelBase):
         #    x = np.array (self.dst_sample_losses)
         #    self.dst_sample_losses = []
         #    b = x[:,1]
-        #    idxs = (x[:,0][ np.argwhere ( b [ b > np.mean(b) ] )[:,0] ]).astype(np.uint)
+        #    idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
         #    generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
             
         if self.options['learn_mask']:
diff --git a/nnlib/nnlib.py b/nnlib/nnlib.py
index 76ec058..9920434 100644
--- a/nnlib/nnlib.py
+++ b/nnlib/nnlib.py
@@ -463,7 +463,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 mask = self.mask
                 if self.is_mse:                
                     blur_mask = tf_gaussian_blur(max(1, mask.get_shape().as_list()[1] // 32))(mask)
-                    return K.mean ( 10*K.square( y_true*blur_mask - y_pred*blur_mask ) )
+                    return K.mean ( 100*K.square( y_true*blur_mask - y_pred*blur_mask ) )
                 else:
                     return (1.0 - (tf.image.ssim (y_true*mask, y_pred*mask, 1.0))) / 2.0
         nnlib.DSSIMMSEMaskLoss = DSSIMMSEMaskLoss