Can someone tell me mathematically how sample_weight and class_weight are used in Keras in the calculation of loss function and metrics? A simple mathematical express will be great.
Effect of class_weight and sample_weight in Keras
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It is a simple multiplication. The loss contributed by the sample is magnified by its sample weight. Assuming
i = 1 to n
samples, a weight vector of sample weightsw
of lengthn
, and that the loss for samplei
is denotedL_i
:In Keras in particular, the product of each sample's loss with its weight is divided by the fraction of weights that are not 0 such that the loss per batch is proportional to the number of weight > 0 samples. Let
p
be the proportion of non-zero weights.Here's the relevant snippet of code from the Keras repo:
class_weight
is used in the same way assample_weight
; it is just provided as a convenience to specify certain weights across entire classes.The sample weights are currently not applied to metrics, only loss.