          for idx in range(self.upscalers_no-2):
              var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**idx)
          var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**(idx + 1))
  
          var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var_x)
  
          outputs = [var_x]
  
          if self.config.get("mask_type", None) is not None:
              var_y = var_xy
 
</s> remove         decoder_a_complexity = int(self.config['complexity_decoder'] / 1.5)
 </s> add         decoder_a_complexity = int(self.config["complexity_decoder"] / 1.5) </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             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") </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") </s> remove         decoder_b_complexity = self.config['complexity_decoder']
 </s> add         decoder_b_complexity = self.config["complexity_decoder"]
        var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
          for idx in range(self.upscalers_no-2):
              var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**idx)
          var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**(idx + 1))
  
        var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
  
          outputs = [var_x]
  
          if self.config.get("mask_type", None) is not None:
              var_y = var_xy
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/realface.py