  
          config = serializer.unmarshal(struct)["config"]
          model_outputs = self._get_outputs(config)
  
          # loss length of unique should be 3:
          # - decoder_both, 1, 2
          # - docoder_a, decoder_b, 1
          split_output = len(np.unique(model_outputs[..., :2])) != 3
          for side_outputs, side in zip(model_outputs, ("a", "b")):
              logger.debug("side: '%s', outputs: '%s'", side, side_outputs)
              layer_name = side_outputs[0][0]
  
              output_config = next(layer for layer in config["layers"]
 
</s> remove             self._samples = dict()
            self._target = dict()
            self._masks = dict()
 </s> add             self._samples = {}
            self._target = {}
            self._masks = {} </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         output = ", ".join(["Loss {}: {:.5f}".format(side, side_loss)
 </s> add         output = ", ".join([f"Loss {side}: {side_loss:.5f}" </s> remove         retval = dict()
 </s> add         retval = {} </s> remove         feeds = dict()
        figures = dict()
        headers = dict()
 </s> add         feeds = {}
        figures = {}
        headers = {}
  
          config = serializer.unmarshal(struct)["config"]
          model_outputs = self._get_outputs(config)
  




          for side_outputs, side in zip(model_outputs, ("a", "b")):
              logger.debug("side: '%s', outputs: '%s'", side, side_outputs)
              layer_name = side_outputs[0][0]
  
              output_config = next(layer for layer in config["layers"]
keep keep keep keep replace replace replace replace 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