mlpack IRC logs, 2018-06-05

Logs for the day 2018-06-05 (starts at 0:00 UTC) are shown below.

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--- Log opened Tue Jun 05 00:00:50 2018
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05:37 < zoq> ShikharJ: We can install the missing python packages, just let us know
05:37 < zoq> ShikharJ: I think numpy is installed for python3.4
05:38 < ShikharJ> zoq: I don't think there is a need now. The dataset is prepared, and the model is currently training. I'll clear up the DCGAN code and get some results on that as well.
05:43 < zoq> ShikharJ: Okay, if you need the packages, at some point just let us know.
05:44 < ShikharJ> zoq: Sure. BTW, did you happen to review the GAN::Gradients code as you were saying?
05:46 < zoq> ShikharJ: Yes, it looks good, as I said it could be improved if we 'outsource' the optimization process.
05:47 < zoq> I think once we get the results, we can merge the code
05:48 < ShikharJ> zoq: Sure, I'll let you know when I get the results.
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07:51 < sumedhghaisas> Atharva: Hi Atharva
07:51 < sumedhghaisas> how are you?
07:55 < Atharva> sumedhghaisas: I am good, what about you? How long will you be in India?
07:56 < sumedhghaisas> doing well, still got a week in India :)
07:56 < Atharva> Oh nice
07:57 < sumedhghaisas> Atharva: is the code compiling on your machine? I mean, the new PR?
07:58 < sumedhghaisas> also have you looked at the failed checks on the PR?
08:02 < sumedhghaisas> Atharva: Also, I am little bit confused about your Backward and Gradient.
08:02 < sumedhghaisas> Does your 'Backward' take into consideration the error signal coming from KL loss?
08:03 < Atharva> I did compile it once before pushing, I think the builds are failing because of some of the later commits.
08:03 < sumedhghaisas> if it does, then shouldn't it be calling the klBackward?
08:03 < Atharva> The memory checks are probably because of the incomplete test I wrote
08:03 < sumedhghaisas> if it does not, then it's a linear equation, how can 'input' parameter be used in the 'Backward'?
08:04 < Atharva> sumedhghaisas: I didn't get you, are you talking about the klBackward function
08:04 < sumedhghaisas> there is namescopeless join_cols in the middle ... that's why I asked :) I think it should be a goodarma::
08:05 < sumedhghaisas> *Arma::join_cols
08:05 < sumedhghaisas> Atharva: maybe the question is little bit confusing...
08:06 < Atharva> Oh, but that compiled on my system, I will change it anyway
08:06 < sumedhghaisas> could you just go over the math you used in Backward?
08:06 < sumedhghaisas> that's strange... hmmm
08:06 < Atharva> Yeah, both of them?
08:07 < Atharva> I mean Backward and klBackward?
08:07 < sumedhghaisas> I wonder where is it getting the symbol join_cols from
08:07 < sumedhghaisas> let's start with Backward
08:08 < Atharva> Yeah, wll you be free in like an hour? I will take a shower and have lunch and get back to this.
08:09 < sumedhghaisas> I have to go out lunch :( and attend a meeting. is 5 IST good for you?
08:09 < Atharva> Also, as Ryan mentioned yesterday, we can use the GaussianDistribution class for univariate as well. The covariance will just be 1 x 1.
08:10 < Atharva> sumedhghaisas: Sure! Till then I will check the math and also the failing builds
08:10 < sumedhghaisas> Atharva: Sounds good!
08:34 < sumedhghaisas> Atharva: My plans got changed a bit so let me know when you are free and we can have a chat
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09:50 < jenkins-mlpack> Project docker mlpack nightly build build #340: SUCCESS in 2 hr 36 min: http://masterblaster.mlpack.org/job/docker%20mlpack%20nightly%20build/340/
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10:25 < Atharva> sumedhghaisas: You there?
10:25 < sumedhghaisas> Atharva: yup :)
10:26 < sumedhghaisas> you wanna go over the math one time?
10:26 < Atharva> I pushed a commit just now, it should fix the build errors. They were mostly due to the fact that I was still using the old name "sampling" in a lot of other places.
10:26 < sumedhghaisas> yes. I also observed that in the PR :)
10:27 < Atharva> Yeah, the let's go through the Backward function first
10:27 < sumedhghaisas> From next time make sure you do clean build before sending the PR, that should fix all these problems
10:28 < sumedhghaisas> I think some make configuration is causing this problem
10:28 < sumedhghaisas> better to do clean build
10:28 < sumedhghaisas> Sure. Backward has 2 gradients right? Mean and stddev
10:28 < Atharva> Yeah, I always do that, I forgot somehow last night, maybe I was sleepy :p
10:29 < Atharva> Yeah
10:29 < sumedhghaisas> I think the backward for mean in the code is correct, which is gy
10:30 < sumedhghaisas> I am little confused about the equation - input - mean / gaissianSample
10:31 < Atharva> There is a good chance I got that wrong, maybe you are correct.
10:31 < Atharva> What do you think it should be>
10:31 < Atharva> ?
10:31 < sumedhghaisas> so out = mean + sample * stddev
10:32 < sumedhghaisas> so g_stddev = gy / sample
10:33 < sumedhghaisas> now you take this error backwards through the Softplus layer and that should give you the error of first vector
10:33 < sumedhghaisas> does that make sense?
10:34 < Atharva> Okay
10:34 < Atharva> I will correct it.
10:34 < sumedhghaisas> also I think you don't have to store mean and stddev... as gradient gets input as a parameter
10:36 < sumedhghaisas> Another thing is, the current computation only passes the error of the decoder to the encoder
10:36 < Atharva> Doesn't the Backward function's input parameter come from the layer after that?
10:36 < sumedhghaisas> the KL error has to be added to it
10:39 < Atharva> Yes, I will do it
10:39 < sumedhghaisas> Atharva: ahh I see. I always get confused by that name. So you mean to say the 'input' parameter to 'Backward' is actually the layer output?
10:39 < Atharva> I think yes, because that's how the Backwardvisitor in ffn works
10:40 < Atharva> SO i don't think it will get the mean and std unless we store it
10:40 < Atharva> So
10:40 < sumedhghaisas> okay. Could you make sure that's the case? I vaguely recall this being the case, but just want to make sire
10:40 < sumedhghaisas> in that case, we need to store mean and stddev
10:41 < Atharva> Yeah I am pretty sure, also it makes sense because the backward function needs the output of the layer and not the input
10:42 < Atharva> I will still check that logic
10:43 < sumedhghaisas> I think you are right
10:43 < Atharva> Yeah, in the Backward function of ffn_impl.hpp, it can be verified
10:44 < sumedhghaisas> Let me know when you make these changes, I will go over the PR again
10:44 < Atharva> What about the klForward and klBackward, is that correct?
10:45 < sumedhghaisas> ahh... I haven't gone over that. Let me check those functions as well.
10:46 < sumedhghaisas> I will also recommend checking the gradients before by writing a test for numerical gradient check
10:46 < sumedhghaisas> you can check how it's done for other layers
10:46 < sumedhghaisas> we need to replicate the same
10:46 < sumedhghaisas> this way you can figure out which gradients are wrong
10:47 < Atharva> Yeah, that is one test, one is just a simple test to see if output is different every time.
10:47 < Atharva> What else can we check?
10:51 < sumedhghaisas> Atharva: Also, have you checked how the Deriv function of SoftPlus works? I think it expects input rather than erro
10:54 < Atharva> Yeah, so won't (input - mean) / gaussian sample be the input for a backward softplus?
11:35 < sumedhghaisas> Atharva: Sorry got caught up in some work
11:36 < sumedhghaisas> I think you have to do a submit to get the actual input to the Softplus
11:36 < sumedhghaisas> *submat :)
11:38 < sumedhghaisas> ahh wait... but you are storing the stddev
11:38 < Atharva> But the input to the Backward function will actually be a latenSize sized matrix
11:39 < sumedhghaisas> hmm... let me see. Let's do a size analysis.
11:39 < sumedhghaisas> so the error coming from up will have a size of B*L
11:39 < sumedhghaisas> where B is batch and L is latent size
11:39 < Atharva> is it L*B?
11:40 < Atharva> I am not sure
11:40 < sumedhghaisas> ahh yes... you are right. Sorry for that.
11:40 < sumedhghaisas> it's column major.
11:40 < sumedhghaisas> so L*B
11:40 < Atharva> Yes
11:40 < sumedhghaisas> error of mean will be L*B
11:41 < sumedhghaisas> error of stddev will be gy / sample... that will be L*B
11:42 < sumedhghaisas> softmax Deriv will be L*B if we pass the submat..
11:42 < sumedhghaisas> so the multiplication will give the error of input which will be L*B
11:43 < sumedhghaisas> this is only the error of second vector of input
11:43 < sumedhghaisas> the first vector error will directly from the error of mean
11:43 < sumedhghaisas> so the concat will give 2L * B
11:43 < sumedhghaisas> does that make sense?
11:45 < sumedhghaisas> ahh by multiplication I mean multiplying the stddev error by Softplus deriv
11:45 < sumedhghaisas> that should be the error of the softplus's error
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11:46 < Atharva> So, are you saying that input param of the Backward function will be 2L * B
11:48 < sumedhghaisas> umm... no no. The input params won't be used here I think, I am saying g will be 2L*B
11:49 < sumedhghaisas> maybe we are on the wrong page :P
11:50 < Atharva> Yeah, g will be 2L * B
11:51 < sumedhghaisas> okay let me suggest changes on the PR
11:51 < sumedhghaisas> maybe that will make things clear
11:52 < Atharva> sorry, I had something to do
11:53 < Atharva> Okay, yeah we can discuss it there
11:59 < sumedhghaisas> Atharva: Just added the changes I think should solve the problem. Let me know what you think.
12:00 < Atharva> Yes, thank you, I will leave comments there
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12:05 < sumedhghaisas> Atharva: Ahh wait. Input is L*B, it's the output of the layer. we really need to change the name. That was causing the confusion in my head. I am so sorry.
12:06 < Atharva> Yes, I used to get confused with that too
12:07 < sumedhghaisas> Although, I would still prefer storing stddev than recomputing it again
12:08 < Atharva> Yes, that's better, I was double minded about whether to store it or not and hence those mistakes.
12:31 < sumedhghaisas> I don't think there were mistakes though. That was my stupidity around 'input'. Sorry for that.
12:32 < sumedhghaisas> Although we do need to add error from KL to the error computed in Backward
12:32 < Atharva> Yes
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15:07 < rcurtin> after I merge the documentation fixes on Friday, I'd be happy to release mlpack 3.0.2 because it changes a lot of the documentation (especially the auto-generated Python binding documentation)
15:07 < rcurtin> does anyone have anything they'd like me to wait for to include in the release? (or anything I should explicitly not include that was merged to master?)
15:08 < rcurtin> I'd be happy to wait a little bit to do the release also, I am in no particular hurry
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15:17 < zoq> rcurtin: If we merge the CF modifications, I would exclude that part for now, we could also delay the merge; whatever is easier for you.
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15:18 < Atharva> rcurtin: I think the ann output/input PR would be a good inclusion in the next release. I might just need this weekend. Is that okay? Or should we include it in the next release?
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15:22 < zoq> We can make another release after GSoC, that includes all changes.
15:22 < zoq> Unless you like to include it now?
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15:26 < rcurtin> Atharva: zoq: I think it's fine to wait. also I think that would be a significant enough change we would have to call it mlpack 3.1.0
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15:27 < rcurtin> if the CF modifications get merged, I'll leave those out of the release, that's no problem at all
15:27 < rcurtin> I'll see if I have time to do the release on Friday, but I may not be able to---I have a paper submission deadline on Friday so I don't know how frantic I will be :)
15:27 < rcurtin> the paper is basically ready but I am still waiting for some comments from friends that I might have to work in. hopefully nobody will suggest too major of a change :)
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15:50 < travis-ci> manish7294/mlpack#15 (lmnn - 4ac77dc : Manish): The build was broken.
15:50 < travis-ci> Change view : https://github.com/manish7294/mlpack/compare/8ae9745163e8...4ac77dc1117d
15:50 < travis-ci> Build details : https://travis-ci.com/manish7294/mlpack/builds/75394129
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16:19 < Atharva> zoq: That PR is independent of my gsoc project, I will try to complete that before Friday so the release does not get delayed. I will let you know by Thursday if I feel it will take more time, in that case it’s better to include it in next release.
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16:29 < zoq> Atharva: okay, sounds good
16:36 < rcurtin> I'd prefer to release a 3.0.2 version without that change, if only so that the JOSS paper about 'mlpack 3' is still closer to accurate about the version number being new
16:37 < rcurtin> but I think it would be no problem to release 3.1.0 shortly after that with the input/output size change
16:37 < rcurtin> let me know if you disagree, my opinion is not particularly strong here
16:38 < ShikharJ> rcurtin: Since the support for batch sizes would probably not be there before Friday, I think the work for GAN should also be excluded if it gets merged before the weekend.
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17:27 < rcurtin> ShikharJ: that sounds reasonable to me
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18:09 < Atharva> rcurtin: Yeah I think we should go with that. We can include it in 3.1.0
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--- Log closed Wed Jun 06 00:00:51 2018