      def display_item_process(self):
          """ Add a single graph to the graph window """
          logger.trace("Adding graph")
          existing = list(self.subnotebook_get_titles_ids().keys())
          for loss_key in self.display_item.loss_keys:
              tabname = loss_key.replace("_", " ").title()
              if tabname in existing:
                  continue
  
              data = Calculations(session=get_config().session,
 
</s> remove             if loss_key.startswith("loss"):
 </s> add             if loss_key.startswith("total"): </s> remove     def compile_mask(self, batch):
        """ Compile the mask into training data """
        logger.trace("Compiling Mask: (side: '%s')", self.side)
        mask = batch[-1]
        retval = list()
        for idx in range(len(batch) - 1):
            image = batch[idx]
            retval.append([image, mask])
        return retval

 </s> add  </s> remove             batch = self.compile_mask(batch)
 </s> add             # Add mask to inputs
            mask = batch[-1]
            batch = [[feed, mask], batch] </s> remove         return tuple(out[0])
 </s> add         return [tuple(shape) for shape in out]

    @property
    def output_shape(self):
        """ The output shape of the model (shape of largest face output) """
        return self.output_shapes[self.largest_face_index]

    @property
    def largest_face_index(self):
        """ Return the index from model.outputs of the largest face
            Required for multi-output model prediction. The largest face
            is assumed to be the final output
        """
        sizes = [shape[1] for shape in self.output_shapes if shape[2] == 3]
        if not sizes:
            return None
        max_face = max(sizes)
        retval = [idx for idx, shape in enumerate(self.output_shapes)
                  if shape[1] == max_face and shape[2] == 3][0]
        logger.debug(retval)
        return retval

    @property
    def largest_mask_index(self):
        """ Return the index from model.outputs of the largest mask
            Required for multi-output model prediction. The largest face
            is assumed to be the final output
        """
        sizes = [shape[1] for shape in self.output_shapes if shape[2] == 1]
        if not sizes:
            return None
        max_mask = max(sizes)
        retval = [idx for idx, shape in enumerate(self.output_shapes)
                  if shape[1] == max_mask and shape[2] == 1][0]
        logger.debug(retval)
        return retval
        display_tabs = sorted(self.display_item.loss_keys)
        if any(key.startswith("total") for key in display_tabs):
            total_idx = [idx for idx, key in enumerate(display_tabs) if key.startswith("total")][0]
            display_tabs.insert(0, display_tabs.pop(total_idx))
        for loss_key in display_tabs:
      def display_item_process(self):
          """ Add a single graph to the graph window """
          logger.trace("Adding graph")
          existing = list(self.subnotebook_get_titles_ids().keys())
        display_tabs = sorted(self.display_item.loss_keys)
        if any(key.startswith("total") for key in display_tabs):
            total_idx = [idx for idx, key in enumerate(display_tabs) if key.startswith("total")][0]
            display_tabs.insert(0, display_tabs.pop(total_idx))
        for loss_key in display_tabs:
              tabname = loss_key.replace("_", " ").title()
              if tabname in existing:
                  continue
  
              data = Calculations(session=get_config().session,
keep keep keep keep replace keep keep keep keep keep
 Training: Add Multi-Output Support

- Name output nodes of models
- Add support for multiple outputs in models and in training
- Update loss output format for cli and gui
- Sort graphs tabs on training
- Fix analysis graph for new loss names
- Multi output support in convert 
lib/gui/display_command.py