title: Using sample_weights with fit_generator()
body: <p>Inside an autoregressive continuous problem, when the zeros take too much place, it is possible to treat the situation as a zero-inflated problem (i.e. ZIB). In other words, instead of working to fit <code>f(x)</code>, we want to fit <code>g(x)*f(x)</code> where <code>f(x)</code> is the function we want to approximate, i.e. <code>y</code>, and <code>g(x)</code> is a function which output a value between 0 and 1 depending if a value is zero or non-zero. </p>
  
  <p>Currently, I have two models. One model which gives me <code>g(x)</code> and another model which fits <code>g(x)*f(x)</code>. </p>
  
  <p>The first model gives me a set of weights. This is where I need your help. I can use the <code>sample_weights</code> arguments with <code>model.fit()</code>. As I work with tremendous amount of data, then I need to work with <code>model.fit_generator()</code>. However, <code>fit_generator()</code> does not have the argument <code>sample_weights</code>. </p>
  
  <p>Is there a work around to work with <code>sample_weights</code> inside <code>fit_generator()</code>? Otherwise, how can I fit <code>g(x)*f(x)</code> knowing that I have already a trained model for <code>g(x)</code>?</p>