                   "\n\t MAE - Mean absolute error will guide reconstructions of each pixel "
                   "towards its median value in the training dataset. Robust to outliers but as "
                   "a median, it can potentially ignore some infrequent image types in the dataset."
                   "\n\t MSE - Mean squared error will guide reconstructions of each pixel "
                   "towards its average value in the training dataset. As an avg, it will be "
                   "suspectible to outliers and typically produces slightly blurrier results.")
          self.add_item(
              section=section,
              title="l2_reg_term",
              datatype=int,
              group="loss",
 
</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                  "between two images. Allows for large color shifts,but maintains the structure "
 </s> add                  "between two images. Allows for large color shifts, but maintains the structure " </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                  "\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) "
                 "towards its average value in the training dataset. As an average, it will be "
                 "susceptible to outliers and typically produces slightly blurrier results.")
                   "\n\t MAE - Mean absolute error will guide reconstructions of each pixel "
                   "towards its median value in the training dataset. Robust to outliers but as "
                   "a median, it can potentially ignore some infrequent image types in the dataset."
                   "\n\t MSE - Mean squared error will guide reconstructions of each pixel "
                 "towards its average value in the training dataset. As an average, it will be "
                 "susceptible to outliers and typically produces slightly blurrier results.")
                 "towards its average value in the training dataset. As an average, it will be "
                 "susceptible to outliers and typically produces slightly blurrier results.")
          self.add_item(
              section=section,
              title="l2_reg_term",
              datatype=int,
              group="loss",
keep keep keep keep replace 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