Github user sethah commented on the issue:
https://github.com/apache/spark/pull/13621
I realize I was a bit unclear now. The results above are from training a
single layer autoencoder and using it to reconstruct the original data. I used
an encoding layer of 32 neurons so the results above are generated from 1.)
encoding 784 dimension input to 32 dimension encoded input and 2.) decoding the
32 dimension vector to 784 dimensions. I will try to work on getting some
specific numbers and do pre-training. For now, I wanted to point out that we
get poor performance with sigmoid units and discuss where the short-term focus
for deep learning in Spark should be.
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