                   "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."
                   "\n\t LogCosh - log(cosh(x)) acts similiar to MSE for small errors and to "
                   "MAE for large errors. Like MSE, it is very stable and prevents overshoots "
                   "when errors are near zero. Like MAE, it is robust to outliers. NB: Due to a bug "
                   "in PlaidML, this loss does not work on AMD cards."
                   "\n\t Smooth_L1 --- Modification of the MAE loss to correct two of its "
                   "disadvantages. This loss has improved stability and guidance for small errors."
 
</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                  "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                  "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         rounding:  [partial] For <class 'int'> and <class 'float'> datatypes this is
 </s> add         rounding:  [partial] For <class 'int'> and <class 'float'> data types this is
                 "susceptible to outliers and typically produces slightly blurrier results."
                 "\n\t LogCosh - log(cosh(x)) acts similar to MSE for small errors and to "
                   "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 "
                 "susceptible to outliers and typically produces slightly blurrier results."
                 "\n\t LogCosh - log(cosh(x)) acts similar to MSE for small errors and to "
                 "susceptible to outliers and typically produces slightly blurrier results."
                 "\n\t LogCosh - log(cosh(x)) acts similar to MSE for small errors and to "
                   "MAE for large errors. Like MSE, it is very stable and prevents overshoots "
                   "when errors are near zero. Like MAE, it is robust to outliers. NB: Due to a bug "
                   "in PlaidML, this loss does not work on AMD cards."
                   "\n\t Smooth_L1 --- Modification of the MAE loss to correct two of its "
                   "disadvantages. This loss has improved stability and guidance for small errors."
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