                   "AMD cards."
                   "\n\t Pixel_Gradient_Difference - Instead of minimizing the difference between "
                   "the absolute value of each pixel in two reference images, compute the pixel to "
                   "pixel spatial difference in each image and then minimize that difference "
                   "between two images. Allows for large color shifts,but maintains the structure "
                   "of the image.")
          self.add_item(
              section=section,
              title="mask_loss_function",
              datatype=str,
 
</s> remove                  "images. Similiar in approach to SSIM. NB: This loss does not currently work on "
 </s> add                  "images. Similar in approach to SSIM. NB: This loss does not currently work on " </s> remove                  "towards its average value in the training dataset. As an avg, it will be "
                 "suspectible to outliers and typically produces slightly blurrier results.")
 </s> add                  "towards its average value in the training dataset. As an average, it will be "
                 "susceptible to outliers and typically produces slightly blurrier results.") </s> remove                  "suspectible to outliers and typically produces slightly blurrier results."
                 "\n\t LogCosh - log(cosh(x)) acts similiar to MSE for small errors and to "
 </s> add                  "susceptible to outliers and typically produces slightly blurrier results."
                 "\n\t LogCosh - log(cosh(x)) acts similar to MSE for small errors and to " </s> remove                  "\n\t rms-prop - Root Mean Square Propogation. Maintains a moving (discounted) "
 </s> add                  "\n\t rms-prop - Root Mean Square Propagation. Maintains a moving (discounted) " </s> remove                  "the image without the facial mask, reconstuction errors will be "
 </s> add                  "the image without the facial mask, reconstruction errors will be "
                 "between two images. Allows for large color shifts, but maintains the structure "
                   "AMD cards."
                   "\n\t Pixel_Gradient_Difference - Instead of minimizing the difference between "
                   "the absolute value of each pixel in two reference images, compute the pixel to "
                   "pixel spatial difference in each image and then minimize that difference "
                 "between two images. Allows for large color shifts, but maintains the structure "
                   "of the image.")
          self.add_item(
              section=section,
              title="mask_loss_function",
              datatype=str,
keep keep keep keep replace keep keep keep keep keep
 Training updates:
  - Expose optimizer epsilon param
  - Add NaN protection
GUI - slider - add support for discreet values 
plugins/train/_config.py