          return KerasModel(input_, var_x)
  
      def decoder_b(self):
          """ RealFace Decoder Network """
          input_filters = self.config['complexity_encoder'] * 2**(self.downscalers_no-1)
          input_width = self.config['input_size'] // self._downscale_ratio
          input_ = Input(shape=(input_width, input_width, input_filters))
  
          var_xy = input_
  
          var_xy = Dense(self.config['dense_nodes'])(Flatten()(var_xy))
 
</s> remove         input_filters = self.config['complexity_encoder'] * 2**(self.downscalers_no-1)
        input_width = self.config['input_size'] // self._downscale_ratio
 </s> add         input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio </s> remove         var_xy = Dense(self.config['dense_nodes'])(Flatten()(var_xy))
 </s> add         var_xy = Dense(self.config["dense_nodes"])(Flatten()(var_xy)) </s> remove         dense_nodes = int(self.config['dense_nodes']/1.5)
 </s> add         dense_nodes = int(self.config["dense_nodes"]/1.5) </s> remove         encoder_complexity = self.config['complexity_encoder']
 </s> add         encoder_complexity = self.config["complexity_encoder"] </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")
        input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio
          return KerasModel(input_, var_x)
  
      def decoder_b(self):
          """ RealFace Decoder Network """
        input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio
        input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio
          input_ = Input(shape=(input_width, input_width, input_filters))
  
          var_xy = input_
  
          var_xy = Dense(self.config['dense_nodes'])(Flatten()(var_xy))
keep keep keep keep 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/realface.py