Comments (1)
That sounds like an overfitting problem: you likely don't have enough data for it to learn to generalize well and make novel output. There are a couple of things you can try:
- Get more training pieces. In my experiments I generally used more than 100 pieces at a time.
- Train it for less time. This may produce lower-quality output but will generally avoid plagiarizing any particular piece.
- Make the network smaller instead of larger. Smaller networks are more constrained in what they can do and thus have a harder time "memorizing" inputs.
Unfortunately there isn't any "magic bullet" that makes this problem go away for all datasets, so it might be good to just try a few of those and see if anything improves the results.
from biaxial-rnn-music-composition.
Related Issues (20)
- would you guide me to get some dataset(music)? HOT 2
- ValueError: When compiling the inner function of scan the following error has been encountered HOT 2
- numpy.repeat TypeError on 32 bit systems HOT 2
- Error HOT 1
- Allocation error HOT 2
- Issue with local_bitwidth() HOT 1
- Weird behaviour HOT 3
- Any way to resume training? HOT 1
- Question HOT 3
- How to load weights? HOT 1
- Any way to train on CPU? HOT 2
- Any way to set output size?
- BUG with this program
- doesn't work, need help HOT 1
- Longer sample mids
- Error
- A question about Bi-axial LSTM structure
- Question about BEAT input HOT 1
- IOError: [Errno 2] No such file or directory: 'output/sample0.mid' HOT 2
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from biaxial-rnn-music-composition.