              filename += "_{}".format(side.upper())
              name += "_{}".format(side)
          filename += ".h5"
          logger.debug("name: '%s', filename: '%s'", name, filename)
          self.networks[name] = NNMeta(str(self.model_dir / filename), network_type, side, network)
  
      def add_predictor(self, side, model):
          """ Add a predictor to the predictors dictionary """
          logger.debug("Adding predictor: (side: '%s', model: %s)", side, model)
          if self.gpus > 1:
 
</s> remove         logger.debug("network_type: '%s', side: '%s', network: '%s'", network_type, side, network)
 </s> add         logger.debug("network_type: '%s', side: '%s', network: '%s', is_output: %s",
                     network_type, side, network, is_output) </s> remove     def add_network(self, network_type, side, network):
 </s> add     def add_network(self, network_type, side, network, is_output=False): </s> remove     def __init__(self, filename, network_type, side, network):
 </s> add     def __init__(self, filename, network_type, side, network, is_output): </s> remove                      "network: %s", self.__class__.__name__, filename, network_type,
                     side, network)
 </s> add                      "network: %s, is_output: %s", self.__class__.__name__, filename,
                     network_type, side, network, is_output) </s> remove     network:      Define network to this.
 </s> add     network:    Define network to this.
    is_output:  Set to True to indicate that this network is an output to the Autoencoder
        self.networks[name] = NNMeta(str(self.model_dir / filename),
                                     network_type,
                                     side,
                                     network,
                                     is_output)
              filename += "_{}".format(side.upper())
              name += "_{}".format(side)
          filename += ".h5"
          logger.debug("name: '%s', filename: '%s'", name, filename)
        self.networks[name] = NNMeta(str(self.model_dir / filename),
                                     network_type,
                                     side,
                                     network,
                                     is_output)
  
      def add_predictor(self, side, model):
          """ Add a predictor to the predictors dictionary """
          logger.debug("Adding predictor: (side: '%s', model: %s)", side, model)
          if self.gpus > 1:
keep keep keep keep replace keep keep keep keep keep
 Add Convolutional Aware Initialization (#795)

* Training: Add Convolutional Aware Initialization config option

* Centralize Conv2D layer for handling initializer

* Add 'is-output' to NNMeta to indicate that network is an output to the Model 
plugins/train/model/_base.py