So, imaging I’m building an RNN, where the input are discrete temporal events. The occurrences of those events are sparse, and the types of events are limited. (i.e, for a time scale of seconds, one only breaths every 20 seconds, and bats eye lids every 10 seconds, coughs at random intervals. )
I have tried to shove the data into an LSTM with very limited success.
Is there any way I can massage the data and or optimize the NN to make it work better?
Increased RNN sequence length, still not so good.
So, I’m parsing music(like) data, so turns out I just need to read up on magenta, a google deeplearning music package and how it works…