Hello,
I am interested in using RNNs for musicology and need to predict several thing at each “time” step : melody, timing, and harmony. I decided to start with a plain old LSTM before moving on to more advanced concepts. Roughly, the architecture looks like this :

The three outputs predictions are fed to their respective loss functions which are then added together for training.
Upon testing, I realized that I am unable to purposely overfit the model by feeding it only one training example : the loss never converges to 0.
I verified carefully the code, taken separately, each of these outputs can be overfitted, but when I add the three losses together I run into trouble.
This means that there is a problem with the design of my network, but I don’t really understand where. Do you have any idea what is going on ?