title: Use both sample_weight and class_weight simultaneously
body: <p>My dataset already has weighted examples. And in this binary classification I also have far more of the first class compared to the second.</p>
  <p>Can I use both <code>sample_weight</code> and further re-weight it with <code>class_weight</code> in the <code>model.fit()</code> function?</p>
  <p>Or do I first make a new array of new_weights and pass it to the fit function as <code>sample_weight</code>?</p>
  <p>Edit:</p>
  <p>TO further clarify, I already have individual weights for each sample in my dataset, and to further add to the complexity, the total sum of sample weights of the first class is far more than the total sample weights of the second class.</p>
  <p>For example I currently have:</p>
  <blockquote>
  <p>y = [0,0,0,0,1,1]</p>
  <p>sample_weights = [0.01,0.03,0.05,0.02, 0.01,0.02]</p>
  </blockquote>
  <p>so the <em>sum of weights</em> for <em>class '0'</em> is <strong>0.11</strong> and for <em>class '1'</em> is  <strong>0.03</strong>. So I should have:</p>
  <blockquote>
  <p>class_weight = {0 : 1. , 1: 0.11/0.03}</p>
  </blockquote>
  <p>I need to use both <code>sample_weight</code> AND <code>class_weight</code> features. If one overrides the other then I will have to create new sample_weights and then use <code>fit()</code> or <code>train_on_batch()</code>.</p>
  <p>So my question is, can I use both, or does one override the other?</p>