          retval = os.path.splitext(basename)[0].lower()
          logger.debug("model name: '%s'", retval)
          return retval
  
      def set_training_data(self):
          """ Override to set model specific training data.
  
              super() this method for defaults otherwise be sure to add """
          logger.debug("Setting training data")
 
</s> remove             if predict:
                raise ValueError("Unable to load training data. Error: {}".format(str(err)))
 </s> add  </s> remove     def load(self, fullpath=None, predict=False):
 </s> add     def load(self, fullpath=None): </s> remove             if predict:
                raise ValueError("Unable to load training data. Error: {}".format(str(err)))
 </s> add  </s> remove         ## PlaidML has a bug regarding the clipnorm parameter
        ## See: https://github.com/plaidml/plaidml/issues/228
        ## Workaround by simply removing it.
        ## TODO: Remove this as soon it is fixed in PlaidML.
        #if keras.backend.backend() == "plaidml.keras.backend":
 </s> add         # # PlaidML has a bug regarding the clipnorm parameter
        # # See: https://github.com/plaidml/plaidml/issues/228
        # # Workaround by simply removing it.
        # # TODO: Remove this as soon it is fixed in PlaidML.
        # if keras.backend.backend() == "plaidml.keras.backend":
    @property
    def models_exist(self):
        """ Return if all files exist and clear session """
        retval = all([os.path.isfile(model.filename) for model in self.networks.values()])
        logger.debug("Pre-existing models exist: %s", retval)
        return retval

          retval = os.path.splitext(basename)[0].lower()
          logger.debug("model name: '%s'", retval)
          return retval
  
    @property
    def models_exist(self):
        """ Return if all files exist and clear session """
        retval = all([os.path.isfile(model.filename) for model in self.networks.values()])
        logger.debug("Pre-existing models exist: %s", retval)
        return retval

      def set_training_data(self):
          """ Override to set model specific training data.
  
              super() this method for defaults otherwise be sure to add """
          logger.debug("Setting training data")
keep keep keep add keep keep keep keep keep
 Fix VRAM issue with reloading models 
plugins/train/model/_base.py