          self.model = model
          from timeit import default_timer as clock
          self._clock = clock
          
  
          #generator = TrainingDataGenerator(self.random_transform_args, 160)                
          # make sre to keep zoom=2 or you won't get 128x128 vectors as input
          #generator = TrainingDataGenerator(self.random_transform_args, 220, 5, zoom=2)
          #generator = TrainingDataGenerator(self.random_transform_args, 180, 7, zoom=2)
          generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=2)        
          
          self.images_A = generator.minibatchAB(fn_A, self.batch_size)
          self.images_B = generator.minibatchAB(fn_B, self.batch_size)
                  
 
</s> remove         loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)        
 </s> add         loss_B = self.model.autoencoder_B.train_on_batch(warped_B, target_B)
        
        self.model._epoch_no += 1         </s> remove #     
 </s> add      </s> remove             time.strftime("%H:%M:%S"), iter_no, self._clock()-when, loss_A, loss_B),
 </s> add             time.strftime("%H:%M:%S"), self.model._epoch_no, self._clock()-when, loss_A, loss_B),
          self.model = model
          from timeit import default_timer as clock
          self._clock = clock
          





          generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=2)        
          
          self.images_A = generator.minibatchAB(fn_A, self.batch_size)
          self.images_B = generator.minibatchAB(fn_B, self.batch_size)
                  
keep keep keep keep replace replace replace replace replace keep keep keep keep keep
 altered trainer (#425)

altered trainer to accommodate with model change 
plugins/Model_OriginalHighRes/Trainer.py