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'RAKEL' redirects here. For the given name 'Rakel', see.In, multi-label classification and the strongly related problem of multi-output classification are variants of the problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to.Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).
You have to use variations of cross entropy function in other to support multilabel classification. In case you have less than one thousand of ouputs you should use, in your case that you have 4000 outputs you may consider as it is faster than the previous.How to compute accuracy using TensorFlow.This depends on your problem and what you want to achieve. If you don't want to miss any object in an image then if the classifier get all right but one, then you should consider the whole image an error. You can also consider that an object missed or missclassiffied is an error.
The latter I think it supported by sigmoidcrossentropywithlogits.How to set a threshold which judges whether a label is positive ornegative. For instance, if the output is 0.80, 0.43, 0.21, 0.01,0.32 and the ground truth is 1, 1, 0, 0, 1, the labels with scores over 0.25 should be judged as positive.Threshold is one way to go, you have to decided which one. But that is some kind of hack, not real multilable classification. For that you need the previous functions I said before.
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