  
          self.backup = Backup(self.model_dir, self.name)
          self.gpus = gpus
          self.configfile = configfile
          self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
                                 use_icnr_init=self.config["icnr_init"],
                                 use_reflect_padding=self.config["reflect_padding"])
          self.input_shape = input_shape
          self.output_shape = None  # set after model is compiled
          self.encoder_dim = encoder_dim
          self.trainer = trainer
  
 
</s> remove         var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var_x)
 </s> add         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid") </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             var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var_y)
 </s> add             var_y = self.blocks.conv2d(var_y, 1,
                                       kernel_size=5,
                                       padding="same",
                                       activation="sigmoid")
  
          self.backup = Backup(self.model_dir, self.name)
          self.gpus = gpus
          self.configfile = configfile



          self.input_shape = input_shape
          self.output_shape = None  # set after model is compiled
          self.encoder_dim = encoder_dim
          self.trainer = trainer
  
keep keep keep keep replace replace 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