          self.model._epoch_no += 1        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
              time.strftime("%H:%M:%S"), self.model._epoch_no, self._clock()-when, loss_A, loss_B),
  =======
          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
              end='\r')
          
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:8], target_B[0:8]), "training using {}, bs={}".format(self.model, self.batch_size))
 
</s> remove <<<<<<< HEAD
 </s> add  </s> remove conv_init = RandomNormal(0, 0.02)
 </s> add  </s> remove =======
import os
import sys

from . import __version__
from .instance_normalization import InstanceNormalization

>>>>>>> 721e80dd773c850769ba6af82daa9f5ec95812bb
 </s> add  </s> remove >>>>>>> 721e80dd773c850769ba6af82daa9f5ec95812bb
 </s> add  </s> remove <<<<<<< HEAD


=======
 </s> add 
          self.model._epoch_no += 1        
                      
          print("[{0}] [#{1:05d}] [{2:.3f}s] loss_A: {3:.5f}, loss_B: {4:.5f}".format(
              time.strftime("%H:%M:%S"), self.model._epoch_no, self._clock()-when, loss_A, loss_B),






              end='\r')
          
  
          if viewer is not None:
              viewer(self.show_sample(target_A[0:8], target_B[0:8]), "training using {}, bs={}".format(self.model, self.batch_size))
keep keep keep keep replace replace replace replace replace replace keep keep keep keep keep
 added OriginalHighRes model state save 
plugins/Model_OriginalHighRes/Trainer.py