Github user JeremyNixon commented on the issue:
https://github.com/apache/spark/pull/13621
I ran the Keras experiment with code up at [[GitHub link]
](https://github.com/JeremyNixon/autoencoder) if anyone wants to build on this
or replicate it.
Running Sethâs example on the training data set, I was able to get the
results below.
I agree that we should add modern activation functions. More importantly,
we should add improved optimizers and a modular API to make this valuable to
real users.
Iâm going to do a code review here and at scalable-deeplearning in the
next few days regardless of the decision we make around this. I think that
these improvements (activation functions, optimizers) should be a part of a
flexible modular library if we want to give users a modern experience.
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