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Cool stuff! Coincidentally I have started on the feature interpretation.

By applying PCA to the last layer before the predictions I get some really cool features, I interpreted the top two features as ‘naked/hairy’ and ‘dog/cat’. Now I can find the hairiest dogs, and the most naked cats:


I’ve shared my notebook here (only accessible by url).
Next I’ll train a linear classifier on these features to learn what features matter most for what breed.

Tips on how to improve the code are welcome by the way. Once I have a better grasp on the library I’ll rewrite this into a proper blog post :slight_smile:

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