              section=section, title="icnr_init", datatype=bool, default=False,
              info="Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
                   "'checkerboard effect' when upscaling the image.")
          self.add_item(
              section=section, title="subpixel_upscaling", datatype=bool, default=False,
              info="Use subpixel upscaling rather than pixel shuffler.\n"
                   "Might increase speed at cost of VRAM")
 
</s> remove     network:      Define network to this.
 </s> add     network:    Define network to this.
    is_output:  Set to True to indicate that this network is an output to the Autoencoder </s> remove         output_size = self.config['output_size']
 </s> add         output_size = self.config["output_size"] </s> remove                       key=lambda x: abs(x - self.config['input_size']))
 </s> add                       key=lambda x: abs(x - self.config["input_size"]))
        self.add_item(
            section=section, title="conv_aware_init", datatype=bool, default=False,
            info="Use Convolution Aware Initialization for convolutional layers\nThis can help "
                 "eradicate the vanishing and exploding gradient problem as well as lead to "
                 "higher accuracy, lower loss and faster convergence.\nNB: Building the model "
                 "will likely take several minutes as the caluclations for this initialization "
                 "technique are expensive.")
              section=section, title="icnr_init", datatype=bool, default=False,
              info="Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
                   "'checkerboard effect' when upscaling the image.")
        self.add_item(
            section=section, title="conv_aware_init", datatype=bool, default=False,
            info="Use Convolution Aware Initialization for convolutional layers\nThis can help "
                 "eradicate the vanishing and exploding gradient problem as well as lead to "
                 "higher accuracy, lower loss and faster convergence.\nNB: Building the model "
                 "will likely take several minutes as the caluclations for this initialization "
                 "technique are expensive.")
          self.add_item(
              section=section, title="subpixel_upscaling", datatype=bool, default=False,
              info="Use subpixel upscaling rather than pixel shuffler.\n"
                   "Might increase speed at cost of VRAM")
keep keep add 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/_config.py