Using sample_weights with fit_generator()
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Inside an autoregressive continuous problem, when the zeros take too much place, it is possible to treat the situation as an zero-inflated problem (i.e. ZIB). In other words, instead of working to fit `f(x)`, we want to fit `g(x)*f(x)` where `f(x)` is the function we want to approximate `y` and `g(x)` is a function which output a value between 0. and 1. depending if a value is zero or non-zero.
Currently, I have two models. One model which gives me `g(x)` and another model which fits `g(x)*f(x)`.
The first model gives me a set of weights. This is where I need your help. I can use the `sample_weights` arguments with `[login to view URL]()`. As I work with tremendous amount of data, then I need to work with `model.fit_generator()`. However, `fit_generator()` does not have the argument `sample_weights`.
Is there a work around to work with `sample_weights` inside `fit_generator()`? Otherwise, how can I fit `g(x)*f(x)` know that I have already a trained model for `g(x)`?
Nº du projet : #18252035
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