                      del times[idx]
  
          logger.debug("Converted to numpy: (data points: %s, timestamps shape: %s, loss shape: %s)",
                       len(data), times.shape, loss.shape)
  
          return times, loss
  
      def _collect_carry_over(self, data):
 
</s> remove         times, loss = (np.array(times, dtype="float64"), np.array(loss, dtype="float32"))
 </s> add         if is_live and not all(len(val) == len(self._loss_labels) for val in loss):
            # TODO Many attempts have been made to fix this for live graph logging, and the issue
            # of non-consistent loss record sizes keeps coming up. In the meantime we shall swallow
            # any loss values that are of incorrect length so graph remains functional. This will,
            # most likely, lead to a mismatch on iteration count so a proper fix should be
            # implemented.

            # Timestamps and loss appears to remain consistent with each other, but sometimes loss
            # appears non-consistent. eg (lengths):
            # [2, 2, 2, 2, 2, 2, 2, 0] - last loss collection has zero length
            # [1, 2, 2, 2, 2, 2, 2, 2] - 1st loss collection has 1 length
            # [2, 2, 2, 3, 2, 2, 2] - 4th loss collection has 3 length

            logger.debug("Inconsistent loss found in collection: %s", loss)
            for idx in reversed(range(len(loss))):
                if len(loss[idx]) != len(self._loss_labels):
                    logger.debug("Removing loss/timestamps at position %s", idx)
                    del loss[idx]
                    del times[idx] </s> remove         if not data:
            logger.debug("No data to cache")
            return
 </s> add  </s> remove                                                  else "_{}".format(idx))
 </s> add                                                  else f"_{idx}") </s> remove         self._tf_version = [int(i) for i in tf.__version__.split(".")[:2]]
 </s> add  </s> remove         vers = self._tf_version
        kwargs = dict(loss_scale="dynamic") if vers[0] == 2 and vers[1] < 4 else dict()
        logger.debug("tf version: %s, kwargs: %s", vers, kwargs)
 </s> add         kwargs = dict(loss_scale="dynamic") if get_tf_version() < 2.4 else {}
        logger.debug("tf version: %s, kwargs: %s", get_tf_version(), kwargs)
        times, loss = (np.array(times, dtype="float64"), np.array(loss, dtype="float32"))
                      del times[idx]
  
        times, loss = (np.array(times, dtype="float64"), np.array(loss, dtype="float32"))
          logger.debug("Converted to numpy: (data points: %s, timestamps shape: %s, loss shape: %s)",
                       len(data), times.shape, loss.shape)
  
          return times, loss
  
      def _collect_carry_over(self, data):
keep add keep keep keep keep keep keep
 Update code to support Tensorflow versions up to 2.8 (#1213)

* Update maximum tf version in setup + requirements

* - bump max version of tf version in launcher
- standardise tf version check

* update keras get_custom_objects  for tf>2.6

* bugfix: force black text in GUI file dialogs (linux)

* dssim loss - Move to stock tf.ssim function

* Update optimizer imports for compatibility

* fix logging for tf2.8

* Fix GUI graphing for TF2.8

* update tests

* bump requirements.txt versions

* Remove limit on nvidia-ml-py

* Graphing bugfixes
  - Prevent live graph from displaying if data not yet available

* bugfix: Live graph. Collect loss labels correctly

* fix: live graph - swallow inconsistent loss errors

* Bugfix: Prevent live graph from clearing during training

* Fix graphing for AMD 
lib/gui/analysis/event_reader.py