Github user avulanov commented on the pull request:
https://github.com/apache/spark/pull/1379#issuecomment-68002991
New results of experiments with optimized ANN and MLOR are below. I used
the same cluster of 6 machines with 12 workers total, mnist8m dataset as train
and the standard mnist test converted to 784 attributes.
- Results
- ANN classifier: training time: 00:16:58 (was 00:47:55); accuracy:
0.9021
- MLOR: training time: 00:09:46 (was 01:30:45); accuracy: 0.9084
- Average step time (reduce at RDDFunctions.scala:112):
- ANN classifier: 23 seconds (was 51 s)
- MLOR: 14 seconds (was 2.1 mins)
The ANN became ~3x and MLOR ~10x faster (!) than before. The current MLOR
is ~60% faster than current ANN. I assume that there are the following
overheads in ANN: 1) it uses back-propagation, so there are two matrix vector
multiplications on forward and backward passes 2) it does rolling the
parameters stored in matrices to the vector form. I will be happy to know how
these overheads can be reduced. We can't compare with previously obtained
accuracy because I used different test sets.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
