Lets say you install your LSTM machine on a road between London and Oxford. And it makes observations. A car with 3 people inside drives past it in one direction 21 sec after previously observed car (in any direction) : the input is {3, LO, 21}. A bus with 43 people drives in other direction 11 sec later : {43, OL, 11}I cant find a definitive general explanation (without references to existing ML packages code) - how your LSTM layer structure should look like to accept those params (there are 3 of them and they are vastly different between themselves but presenting them together at each step is very significant (as opposed to splitting them to 3 streams and feeding then into 3 LSTMs and then pooling the results).Could someone explain it in a single formula or a drawing?
Multi-param LSTM input
machine learning;lstm