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In Keras (tensorflow backend) I can weight the classes via a parameter, and there is another parameter that allows the weighting of the samples (as it was referenced in this question.)

In my case, I need to do both simultaneously: each sample should have an individual weight for each of the two classes that I have. My problem is that I don't just have one weight per sample for both classes, but the two classes need individual weights for each sample.

How can I achieve this?

Nickpick
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  • This question is a bit difficult to understand. Can you provide an example/more detail? E.g. what is the dimensionality of your data, what weights per class do you want, and what weights per sample? – StatsSorceress May 14 '18 at 14:46

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