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I just read a great post here. I am curious about content of "An example with images" in that post. If the hidden states mean a lot of features of the original picture and getting closer to final result, using dimension reduction on hidden states should provide better result than the original raw pixels, I think.

Hence, I tried it on mnist digits with 2 hidden layers of 256 unit NN, using T-SNE for dimension reduction; the result is far from ideal. From left to right, top to bot, they are raw pixels, second hidden layer and final prediction. Can anyone explain that?

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  • $\begingroup$ How good is your network at performing the task? $\endgroup$
    – Franck Dernoncourt
    Jul 21, 2016 at 14:30
  • $\begingroup$ @FranckDernoncourt, Thanks for your replay. The accuracy is around 94.x%, which is able to distinguish most of digits. Would that be a reason to cause bad performance on dimension reduction? $\endgroup$
    – Hanyu Guo
    Jul 21, 2016 at 19:14

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