I'm trying to train a LSTM recurrent neural network where my data consists of a sequence of animal migration data
((latitude, longitude), (ocean temperature at that point)) pairs. Basically what I want to do is, after training the LSTM with a bunch of these sequences, give the LSTM an initial data pair and have it predict the entire migration path sequence of these pairs.
Problem is, I don't want the LSTM to output the temperature at any point. I want the basic step to be feeding in a
((latitude, longitude), (ocean temperature at that point)) point, and having the LSTM output a
(latitude, longitude). I would then pair that point with the specific temperature at that point, add it to the current sequence, and feed it back into the RNN.
So the problem is not feeding in the sequence of increasing (variable) size, but the training of the model. If I want to train the LSTM with a sequence of
((latitude, longitude), (ocean temperature at that point)) pairs, then how will the LSTM know to only output the latitude and longitude? Can I just have it output the pair with temperature and then remove it? I feel like that would be wasting a lot of computing time and my dataset is quite large.
Any advice or criticism is welcome.