mlpack IRC logs, 2018-07-11

Logs for the day 2018-07-11 (starts at 0:00 UTC) are shown below.

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--- Log opened Wed Jul 11 00:00:41 2018
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07:41 < jenkins-mlpack2> Project docker mlpack nightly build build #3: STILL FAILING in 3 hr 26 min: http://ci.mlpack.org/job/docker%20mlpack%20nightly%20build/3/
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09:21 < travis-ci> manish7294/mlpack#60 (evalBounds - cb36f2d : Manish): The build was broken.
09:21 < travis-ci> Change view : https://github.com/manish7294/mlpack/compare/2d064506b559...cb36f2d70220
09:21 < travis-ci> Build details : https://travis-ci.com/manish7294/mlpack/builds/78655812
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14:25 < travis-ci> manish7294/mlpack#61 (impBounds - 1b7da33 : Manish): The build is still failing.
14:25 < travis-ci> Change view : https://github.com/manish7294/mlpack/compare/1bc7dc0a2a02...1b7da3383136
14:25 < travis-ci> Build details : https://travis-ci.com/manish7294/mlpack/builds/78690580
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16:02 -!- ShikharJ [Elite21812@gateway/shell/elitebnc/x-reuzniqdmakqvved] has joined #mlpack
16:40 < ShikharJ> zoq: Are you there?
16:41 < zoq> ShikharJ:About to step out, but I'm here now.
16:42 < ShikharJ> zoq: I implemented the EvaluateWithGradient function for FFN class, but I don't see an improvement in the runtime, as it hardly reduces just a single if-check. Should I close the PR?
16:43 < ShikharJ> zoq: FFN's Evaluate and Gradient functions are pretty disjoint, so I don't think implementing EvaluateWithGradient would make much sense here.
16:44 < zoq> ShikharJ: It should at least save one Evalute call.
16:45 < zoq> ShikharJ: What do you mean with "it hardly reduces just a single if-check"?
16:46 < ShikharJ> zoq: Yes, but if we call Evaluate and Gradient independently, and just call EvaluateWithGradient, there's practcally no difference in the number and type of functions being called.
16:46 < ShikharJ> zoq: This was different in the case of GAN class, where both Evaluate and Gradient had a Forward() call, which we could reduce in EvaluateWithGradient().
16:49 < zoq> If EvaluateWithGradient isn't implemented the optimizer should call:
16:50 < zoq> Evaluate() for the loss calculation and Gradient() for the Gradient calculation, the Gradient() does also call Evaluate() to to perform the forward pass for the backward and gradient step. EvaluateWithGradient will use the loss from the Gradient step, so this should at least save us one call of Evaluate() in every epoch.
16:51 < zoq> let me check the PR
16:51 < ShikharJ> zoq: Ah, I see it now. I had thought, calling Evaluate() would be unnecessary here, but yeah, that would be an improvement. Please take a look whenever free.
16:56 < zoq> ShikharJ: Okay, see my comment on the PR.
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18:11 < ShikharJ> zoq: I think, we can also make use of FFN::EvaluateWithGradient() internally inside GAN::EvaluateWithGradient() as well. I'll see what can be done.
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20:19 < ShikharJ> zoq: Seems like it is possible! This should give a good speedup as well!
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22:48 < zoq> ShikharJ: Great, I'll take a closer look at the PR tomorrow.
--- Log closed Thu Jul 12 00:00:43 2018