          input_ = Input(shape=(input_width, input_width, input_filters))
  
          var_xy = input_
  
          var_xy = Dense(self.config['dense_nodes'])(Flatten()(var_xy))
          var_xy = Dense(self.dense_width * self.dense_width * self.dense_filters)(var_xy)
          var_xy = Reshape((self.dense_width, self.dense_width, self.dense_filters))(var_xy)
          var_xy = self.blocks.upscale(var_xy, self.dense_filters)
  
          var_x = 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         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         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         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")
        var_xy = Dense(self.config["dense_nodes"])(Flatten()(var_xy))
          input_ = Input(shape=(input_width, input_width, input_filters))
  
          var_xy = input_
  
        var_xy = Dense(self.config["dense_nodes"])(Flatten()(var_xy))
          var_xy = Dense(self.dense_width * self.dense_width * self.dense_filters)(var_xy)
          var_xy = Reshape((self.dense_width, self.dense_width, self.dense_filters))(var_xy)
          var_xy = self.blocks.upscale(var_xy, self.dense_filters)
  
          var_x = 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