Sorry if this is basic or covered elsewhere, I am just starting here and I wasn't able to find an answer, but I might have not been searching for the right thing. So:
I am training a neural network to predict current draw in a system. There are a number of obvious numerical inputs, like temperature, counting rate, voltage, etc.
The most predictive thing, however, is what operation the system is doing. So like, if it's doing a 'calibration' then the current profile is much different than if it's in 'standby'. I know that I can just use a different network for each operation, but in this case I have a couple hundred different macros defined and I don't want to have 200+ neural networks retrain all the time.
I also know that I can have a digital value as an input, but my understanding is that it has to be either 0/1. Also, the relationship to operation is not at all correlated - so operation 100 is not necessarily more current draw than 99 or less than 101.
So, is there a way to have an operation ID or something factor in, but not have it be in the linear combination mathematically? So, basically, tell the system to do a different training based on ID or something? I'll be using python and scikit-learn.