Comments (2)
caozhangjie
commented on August 22, 2024
As described in the paper, exp(-H) takes values between 1/C and 1, where C is the number of classes. Namely, the maximal and minimal values of exp(-H) differ by C times, which may penalize too much for some examples. Hence, we use 1+exp(-H), the reciprocal of the sigmoid function, to shape the weights between (roughly) 1 and 2, which stabilizes training.
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shopeth
commented on August 22, 2024
I have a question on line 33 in here. coeff records the training progress. How does it change the entropy calculation?
Thank you for sharing the code.
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