Busy writing thesis (especially MC chapter to give it to you (David) and afterwards Silvia).
Investigation on cluster flags:
- First ideas about training on flags directly failed because for most clusters the flags are 0 -> Network just learns to produce 0 for everything, so this needs downsampling
- Second idea learn the cluster overlap. This works to some extend

- Two problems need mitigation:
- Also many clusters with overlap value of exactly 0 -> Removed from training data set now to avoid overtraining.
- Network output not bound to be between 0 and 1 without non-linear activation in the last layer: Outputs < 0 are mapped to 0 (sometimes incorrectly)
- Ultimate problem: Cluster overlap does not nicely correlate with cluster flags:

