          _, 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)        
                          
          print("[{0}] [#{1:05d}] loss_A: {2:.5f}, loss_B: {3:.5f}".format(
              time.strftime("%H:%M:%S"), iter_no, loss_A, loss_B),
              end='\r')
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:24], target_B[0:24]), "training using {}, bs={}".format(self.model, self.batch_size))
              
 
</s> remove             viewer(self.show_sample(target_A[0:24], target_B[0:24]), "training using {}, bs={}".format(self.model, self.batch_size))
 </s> add             viewer(self.show_sample(target_A[0:8], target_B[0:8]), "training using {}, bs={}".format(self.model, self.batch_size)) </s> remove   
 </s> add         when = self._clock() </s> remove         generator = TrainingDataGenerator(self.random_transform_args, 160, 6, zoom=2)
 </s> add  </s> remove #TRANSFORM_PRC = 150.
 </s> add 
                    
        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),
          _, 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)        
                    
        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),
                    
        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),
                    
        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),
              end='\r')
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:24], target_B[0:24]), "training using {}, bs={}".format(self.model, self.batch_size))
              
keep keep keep keep replace replace replace keep keep keep keep keep
 OriginalHighRes 128 model update (#418)

Required for OriginalHighRes Model to function 
plugins/Model_OriginalHighRes/Trainer.py