  from __future__ import absolute_import
  
  import logging
  
  from keras import backend as K
  
  import numpy as np
  import tensorflow as tf
  
  from plaidml.op import extract_image_patches
  
 
</s> remove                  "from float32.\nWhile mixed precision will run on most Nvidia models, it will "
 </s> add                  "from float32.\n\nWhile mixed precision will run on most Nvidia models, it will " </s> remove             if get_backend() == "amd":
                self._funcs[output_name] = loss_func
            else:
                self._funcs.setdefault(output_name, []).append(loss_func)
        logger.debug(self._funcs)
 </s> add             self._funcs[output_name] = loss_func
        logger.debug("functions: %s", self._funcs) </s> remove                 loss_func = selected_loss
 </s> add                 if (self._config["loss_function"] in self._uses_l2_reg
                        and self._config["l2_reg_term"] != 0):
                    loss_funcs = [selected_loss, self._loss_dict["mse"]]
                    loss_weights = [1.0, self._config["l2_reg_term"] / 100.0]
                else:
                    loss_funcs = [selected_loss]
                    loss_weights = [1.0]

                if self._config["penalized_mask_loss"]:
                    loss_funcs = [losses.PenalizedLoss(loss) for loss in loss_funcs]

                loss_func = losses.LossWrapper(loss_functions=list(zip(loss_funcs, loss_weights)))

  from __future__ import absolute_import
  
  import logging
  


  import numpy as np
  import tensorflow as tf
  
  from plaidml.op import extract_image_patches
  
keep keep keep keep replace replace keep keep keep keep keep
 Training - Add L2 Regularization to Structural Losses 
lib/model/losses_plaid.py