This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
I see that previously you answered that "for big amount of data you can fit model several times"(#8).
But I didn't work with pytorch before and don't know how it is must work: how pass info about losses and gradients for different parts of dataset.
That's why I want to ask if your library has ability to fit with custom data generator (like fit_generator in keras). Or maybe you can tell me where I can see example for such case.
This is what my class for data looks like(prevoiusly I save different parts of data in "data.npz"):
I see that previously you answered that "for big amount of data you can fit model several times"(#8). But I didn't work with pytorch before and don't know how it is must work: how pass info about losses and gradients for different parts of dataset. That's why I want to ask if your library has ability to fit with custom data generator (like fit_generator in keras). Or maybe you can tell me where I can see example for such case.
This is what my class for data looks like(prevoiusly I save different parts of data in "data.npz"):
And this is how I create generator:
P.S.: I'll be happy to receive any help, because I don't even sure that I go in the right direction.