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I have an LSTM model in Keras for categorical classification (20 possible categories). In many cases, my data can fit multiple categories.

Obviously, my current model uses one-hot encoding and fits on that - that gives me accuracy and validation rates in the 50-60% but I want to improve that by comparing how the model does against the top 3 categories that the algorithm chooses.

Right now, I use Keras with categorical_crossentropy. I presume that this checks to see if the label is the top match and bases the accuracy on that matching. How can I modify the fit/training of the model to allow the labeled category to be in the top 3 (or top X-number) of matches for the accuracy score?

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    $\begingroup$ Are you talking about multi-label categorical classification then? If yes this will change how you think. $\endgroup$
    – TwinPenguins
    Oct 31, 2018 at 7:19

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You are looking for top-k categorical accuracy. This is actually implemented in keras, but you might want to change the k, which can be done with partial function. This is the related link.

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If you're doing multilabel, you should do binary-crossentropy and sigmoid in a final layer. You must score your labels separately. Here's an example.

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