Keras lightest implementation for focal loss function.
Great mathematical solution for optimizing scenarios of unbalanced-classes. Focal loss down-weights the well-classified examples (boosting-like concept). This has the net effect of putting more training emphasis on that data that is hard to classify.
...Hence if an example is easily classified, then its probability p would be >> 0.5(close to 0.9–1.0) and 1 — p which is close to zero causes C.E to produce a very small value, ending up in very low or no learning for that example. The term γ is the focusing parameter which adjusts the rate at which easy examples are downweighted...
from focal_loss import focal_loss
model.compile(loss=[focal_loss()], metrics=["accuracy"], optimizer=adam)