I see there are lots of useful metrics in the evaluate.py such as mse or mae, but I am try to implement my own loss function (ie. logcosh in Keras), is there any method we can invoke the self-defined functions from a seperate python module rather than modifying the evaluate.py itself?
I think the solution would have to be slightly more complicated. I looked at the implementation of evaluate.py, there are checks in there to see if the metric is supervised or not. (only because of log_likelihood) If custom metrics are to be added, there will be a flag to check whether it is supervised or unsupervised. This is unless of course, the implicit assumption if your solution is valid for most use cases.