                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
          self.add_item(
              section=section,
              title="convert_batchsize",
              datatype=int,
              default=16,
 
</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                  "the image without the facial mask, reconstuction errors will be "
 </s> add                  "the image without the facial mask, reconstruction errors will be " </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 "
        self.add_item(
            section=section,
            title="nan_protection",
            datatype=bool,
            default=True,
            group="network",
            info="If a 'NaN' is generated in the model, this means that the model has corrupted "
                 "and the model is likely to start deteriorating from this point on. Enabling NaN "
                 "protection will stop training immediately in the event of a NaN. The last save "
                 "will not contain the NaN, so you may still be able to rescue your model.",
            fixed=False)
                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
        self.add_item(
            section=section,
            title="nan_protection",
            datatype=bool,
            default=True,
            group="network",
            info="If a 'NaN' is generated in the model, this means that the model has corrupted "
                 "and the model is likely to start deteriorating from this point on. Enabling NaN "
                 "protection will stop training immediately in the event of a NaN. The last save "
                 "will not contain the NaN, so you may still be able to rescue your model.",
            fixed=False)
          self.add_item(
              section=section,
              title="convert_batchsize",
              datatype=int,
              default=16,
keep keep keep add 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