You'll probably have to train separate char-rnn instances, unfortunately. For the past week or two, I've been experimenting with putting in a metadata prefix which I can use as a seed to specify author/ style, but thus far it hasn't worked at all. The char-rnn just spits out a sort of average text and doesn't mimic specific styles.
Yup. That's been my finding as well. char-rnn was really just a diversion of curiosity after I'd cleaned up the data. My best idea right now is to make a generative model of p(next_token | previous_token(s), author), essentially connecting author directly to every observation. I'm mostly sure that using characters as tokens is overkill for this and requires a higher complexity model than I can afford with this dataset and my computational resources, so I'm going to stop using char-rnn with it.
That's possible. My hope was that you could get authorial style by just including it inline as metadata rather than needing to hardwire it into the architecture (eg take 5 hidden nodes and have them specify an ID for the author so the RNN can't possibly forget). It would have been so convenient and made char-rnn much more useful, but I guess it's turning out to be too convenient to be true.
You'll probably have to train separate char-rnn instances, unfortunately. For the past week or two, I've been experimenting with putting in a metadata prefix which I can use as a seed to specify author/ style, but thus far it hasn't worked at all. The char-rnn just spits out a sort of average text and doesn't mimic specific styles.