          ## Workaround by simply removing it.
          ## TODO: Remove this as soon it is fixed in PlaidML.
          #if keras.backend.backend() == "plaidml.keras.backend":
          #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
          #else:
          #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, clipnorm=1.0)
          optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
  
          for side, model in self.predictors.items():
              loss_names = ["loss"]
 
</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": </s> remove                 is_loaded = network.load(fullpath=model_mapping[network.side][network.type],
                                         predict=self.predict)
 </s> add                 is_loaded = network.load(fullpath=model_mapping[network.side][network.type]) </s> remove                 is_loaded = network.load(predict=self.predict)
 </s> add                 is_loaded = network.load()
        # else:
          ## Workaround by simply removing it.
          ## TODO: Remove this as soon it is fixed in PlaidML.
          #if keras.backend.backend() == "plaidml.keras.backend":
          #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
        # else:
          #    optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, clipnorm=1.0)
          optimizer = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
  
          for side, model in self.predictors.items():
              loss_names = ["loss"]
keep keep keep keep replace keep keep keep keep keep
 Fix VRAM issue with reloading models 
plugins/train/model/_base.py