diff --git a/models/Model_Quick96/Model.py b/models/Model_Quick96/Model.py
index 51d675b..e21957d 100644
--- a/models/Model_Quick96/Model.py
+++ b/models/Model_Quick96/Model.py
@@ -143,8 +143,8 @@ class Quick96Model(ModelBase):
                     self.CA_conv_weights_list += [layer.weights[0]] #- is Conv2D kernel_weights
 
         if self.is_training_mode:
-            self.src_dst_opt      = RMSprop(lr=2e-4)
-            self.src_dst_mask_opt = RMSprop(lr=2e-4)
+            self.src_dst_opt      = RMSprop(lr=2e-4, lr_dropout=0.3)
+            self.src_dst_mask_opt = RMSprop(lr=2e-4, lr_dropout=0.3)
                 
             target_src_masked = self.model.target_src*self.model.target_srcm
             target_dst_masked = self.model.target_dst*self.model.target_dstm
diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 1ee851f..c1750b9 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -452,9 +452,9 @@ class SAEHDModel(ModelBase):
         psd_target_dst_anti_masked = self.model.pred_src_dst*(1.0 - target_dstm)
 
         if self.is_training_mode:
-            self.src_dst_opt      = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
-            self.src_dst_mask_opt = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
-            self.D_opt            = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
+            self.src_dst_opt      = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
+            self.src_dst_mask_opt = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
+            self.D_opt            = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
         
             src_loss =  K.mean ( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_opt, pred_src_src_masked_opt) )
             src_loss += K.mean ( 10*K.square( target_src_masked_opt - pred_src_src_masked_opt ) )