unrealwill commented on issue #8025: How to use softlabel in MXNET? URL: https://github.com/apache/incubator-mxnet/issues/8025#issuecomment-332154231 @xuliuac I'm not quite familiar with mxnet yet, but I think that metric and losses are two different things. If mxnet is anything like Keras, Metrics are being used for reporting only (additional info), and loss being used for actual training. (If I didn't make a sign error) The loss won't converge to 0 (you can calculate the optimum value the loss could attain : (sum(-log(max(label)) )) (a soft label is similar to training set error (two training examples with different label) like saying here is this example I don't know if it's a 3 or a 8 but I'm 65% confident it's a 8 ), but it should converge towards a network trying to reach certainty in its prediction (a deterministic policy) (It will predict a 8 100%, but not get penalized too much if he predict a 3 ). If you try to learn a stochastic policy then use a Kullback-Leibler divergence between you prediction and your soft label. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
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