          """ RealFace Encoder Network """
          input_ = Input(shape=self.input_shape)
          var_x = input_
  
          encoder_complexity = self.config['complexity_encoder']
  
          for idx in range(self.downscalers_no - 1):
              var_x = self.blocks.conv(var_x, encoder_complexity * 2**idx)
              var_x = self.blocks.res_block(var_x, encoder_complexity * 2**idx, use_bias=True)
              var_x = self.blocks.res_block(var_x, encoder_complexity * 2**idx, use_bias=True)
 
</s> remove         decoder_b_complexity = self.config['complexity_decoder']
 </s> add         decoder_b_complexity = self.config["complexity_decoder"] </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         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)
        encoder_complexity = self.config["complexity_encoder"]
          """ RealFace Encoder Network """
          input_ = Input(shape=self.input_shape)
          var_x = input_
  
        encoder_complexity = self.config["complexity_encoder"]
  
          for idx in range(self.downscalers_no - 1):
              var_x = self.blocks.conv(var_x, encoder_complexity * 2**idx)
              var_x = self.blocks.res_block(var_x, encoder_complexity * 2**idx, use_bias=True)
              var_x = self.blocks.res_block(var_x, encoder_complexity * 2**idx, use_bias=True)
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