          timestamps = []
          last_step = -1
          live_data = is_training and session_id == max(self._log_filenames)
  
          if live_data:
              iterator = self._get_latest_live()
          else:
 
</s> remove                     loss.append(step)
 </s> add                     if last_step != 0:
                        loss.append(step) </s> remove         loss = np.array(loss, dtype="float32")
        timestamps = np.array(timestamps, dtype="float64")
 </s> add         try:
            loss = np.array(loss, dtype="float32")
        except ValueError as err:
            # When collecting live loss, the current batch may not be completely populated
            # Carry over the last loss to the next collection
            if "setting an array element with a sequence" in str(err):
                carry_over = loss[-1]
                logger.debug("Carrying over data: (carry_over: %s, new loss: %s)",
                             carry_over, loss[:-1])
                loss = np.array(loss[:-1], dtype="float32")
            else:
                raise </s> remove                 lbl = tag.replace("batch_", "")
 </s> add                 # tf2.3 stopped respecting loss names in tensorboard callback so rewrite
                lbl_split = tag.replace("batch_", "").replace("_loss", "").split("_")
                if lbl_split[-1] in ("a", "b"):
                    lbl = "face_{}".format(lbl_split[-1])
                else:
                    lbl = "mask_{}".format(lbl_split[-2])
        carry_over = None
          timestamps = []
          last_step = -1
        carry_over = None
          live_data = is_training and session_id == max(self._log_filenames)
  
          if live_data:
              iterator = self._get_latest_live()
          else:
keep add keep keep keep keep keep
 Update to TF2.3 (#1100)

* Minimum requirements to tf2.3.
  - Handle upgrades for Windows users with tf2.2 installed by Pip
  - Handle windows upgrade from pip tf2.2
  - Explicitly install Cuda for Conda installs

* Update tensorflow errors api reference
* Suppress AutoGraph warning messages
* Update GUI Stats to work with tf2.3
* Fix live graph for tf2.3
* DSSIMObjective - autoGraph bugfix
* Update Travis test 
lib/gui/stats.py