Relegator UPDATE

Relegator (as well as regressor and binary classifiers) is now working on the massive moons dataset with Tensorflow2.0.  Currently running with a modified cross-entropy plug inverse significance loss function.  The classifier does not seem to want to assign any events to the relegation class.  SO, a couple ideas:

1. Doe the significance part of the loss function need to be computed using tf functions (so that gradients are supplied)?
2. Need to try different ways to incorporate the significance into the loss function.  
3. It might be necessary to train on TWO datasets: an even-population dataset for the accuracy (relegator cross-entropy) and a weighted-population dataset for the significance.  
4. It might be necessary to make the moons dataset have relatively more background at the intersection.  Does that make sense?  I mean that the distribution of background events may need to be nonuniform around the arc, with a larger density of background events (and lower density of signal events) in the region of overlap.