          _, warped_A, target_A = next(self.images_A)
          _, warped_B, target_B = next(self.images_B)
  
          loss_A = self.model.autoencoder_A.train_on_batch(warped_A, target_A)
  <<<<<<< HEAD
          loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
          
          self.model._epoch_no += 1        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
 
</s> remove =======
        loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)        
                    
        print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
            time.strftime("%H:%M:%S"), iter_no, self._clock()-when, loss_A, loss_B),
>>>>>>> 721e80dd773c850769ba6af82daa9f5ec95812bb
 </s> add  </s> remove <<<<<<< HEAD
 </s> add  </s> remove <<<<<<< HEAD


=======
 </s> add  </s> remove <<<<<<< HEAD
        
        x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
        
        return KerasModel(inpt, x)
    
    
=======
 </s> add  </s> remove <<<<<<< HEAD

=======
>>>>>>> 721e80dd773c850769ba6af82daa9f5ec95812bb
 </s> add 
          _, warped_A, target_A = next(self.images_A)
          _, warped_B, target_B = next(self.images_B)
  
          loss_A = self.model.autoencoder_A.train_on_batch(warped_A, target_A)

          loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
          
          self.model._epoch_no += 1        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
keep keep keep keep replace keep keep keep keep keep
 added OriginalHighRes model state save 
plugins/Model_OriginalHighRes/Trainer.py