                              strides=strides,
                              padding=padding,
                              **kwargs)
          if use_instance_norm:
              var_x = InstanceNormalization()(var_x)
          if not res_block_follows:
              var_x = LeakyReLU(0.1)(var_x)
          return var_x
  
      def upscale(self, inp, filters, kernel_size=3, padding="same",
 
</s> remove             var_x = LeakyReLU(0.1)(var_x)
 </s> add             var_x = LeakyReLU(0.1, name="{}_leakyrelu".format(name))(var_x) </s> remove             var_x = InstanceNormalization()(var_x)
 </s> add             var_x = InstanceNormalization(name="{}_instancenorm".format(name))(var_x) </s> remove             var_x = LeakyReLU(0.1)(var_x)
 </s> add             var_x = LeakyReLU(0.1, name="{}_leakyrelu".format(name))(var_x) </s> remove             var_x = SubPixelUpscaling()(var_x)
 </s> add             var_x = SubPixelUpscaling(name="{}_subpixel".format(name))(var_x)
            var_x = InstanceNormalization(name="{}_instancenorm".format(name))(var_x)
                              strides=strides,
                              padding=padding,
                              **kwargs)
          if use_instance_norm:
            var_x = InstanceNormalization(name="{}_instancenorm".format(name))(var_x)
          if not res_block_follows:
              var_x = LeakyReLU(0.1)(var_x)
          return var_x
  
      def upscale(self, inp, filters, kernel_size=3, padding="same",
keep keep keep keep replace keep keep keep keep keep
 Model Structure Changes

Standardize inputs for AutoEncoders
Name outputs from AutoEncoders
Name layers in nn_blocks.py 
lib/model/nn_blocks.py