fast, flexible C++ machine learning library

mlpack IRC logs, 2018-03-21

Logs for the day 2018-03-21 (starts at 0:00 UTC) are shown below.

March 2018
--- Log opened Wed Mar 21 00:00:01 2018
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00:15 < travis-ci> mlpack/mlpack#4469 (master - 97d8883 : Marcus Edel): The build passed.
00:15 < travis-ci> Change view : https://github.com/mlpack/mlpack/compare/1ee8268cd51b...97d88838c0b5
00:15 < travis-ci> Build details : https://travis-ci.org/mlpack/mlpack/builds/356114228
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01:24 < MystikNinja> zoq: Is there any way I could view the lrsdp.hpp code from when the mvu.cpp code was written? There are methods of the LRSDP object like A(), B() and C() which I can't figure out the function of.
01:25 < MystikNinja> Those methods are being used in the mvu.cpp code, but they are nowhere to be found in the current lrsdp.hpp code
01:38 < rcurtin> MystikNinja: just check out the last revision of when mvu.cpp was modified
01:44 < MystikNinja> Whew, the relevant code is from way back in 2012!
01:44 < MystikNinja> Thanks rcurtin, this helps
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02:29 < rcurtin> MystikNinja: yeah, it was a long time ago now...
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03:17 < ricklly> hello, everyone, I'm intrested in MVU, a fun project!
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07:23 < Atharva> sumedhghaisas: on the gsoc ideas page, for the VAE project, it has been mentioned that we need to reproduce the results from the two papers. Should this be done along with the unit tests for the framework, or will it be better if I make sample models in mlpack/models and reproduce them there
07:24 < Atharva> Because trying to do this along with the unit tests doesn’t seem right, we will be testing entire models.
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11:08 < sumedhghaisas> @Atharva: unit tests will involve checking functionality, models repository would be ideal to reproduce the results
11:09 < sumedhghaisas> apart from reproducing results, units tests should check the API that you have built from VAE
11:09 < Atharva> Okay, that’s what I will propose.
11:10 < sumedhghaisas> check feedforward and recurrent tests for examples
11:10 < Atharva> Yeah
11:11 < Atharva> I am writing my proposal in markdown, i will upload the draft as soon as I can
11:18 < sumedhghaisas> good to hear :)
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13:14 < travis-ci> ShikharJ/mlpack#114 (ConvolutionalLayer - 49e3cda : Shikhar Jaiswal): The build has errored.
13:14 < travis-ci> Change view : https://github.com/ShikharJ/mlpack/compare/4128d92aabb6...49e3cda97d5d
13:14 < travis-ci> Build details : https://travis-ci.org/ShikharJ/mlpack/builds/356321721
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13:25 < ravikiran0606> Hello, I am Ravi Kiran S, doing my 3-rd year B.E Computer Science and Engineering at Anna University-College of Engineering, Guindy Campus.
13:25 < ravikiran0606> I am very much interested to contribute to mlpack project. I would like to work on "String Processing Utilities" and "Essential Deep Learning Modules". Is there any preliminary set of tasks that I need to do before applying to GSoC 2018. And I would like to submit my draft proposal soon. I would like to know whether any template is available for GSoC proposal ?.
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13:31 < zoq> ravikiran06: Hello there, you don't have to submit a patch or something like that, however it might be helpful. About the application check: https://github.com/mlpack/mlpack/wiki/Google-Summer-of-Code-Application-Guide
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13:47 < Atharva> zoq: What do you think about the discussion on VAEs on yesterday’s irc logs?
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13:56 < yashsharan> Hello I was working on the issue of implementing a new OpenAi environments and I had some doubts
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14:37 < zoq> Atharva: I'll have to think about it, but introducing a sperate class is probably the easiest.
14:38 < zoq> yashsharan: Let me know if you need help.
14:40 < Atharva> zoq: yes, for now that’s what I will be proposing, I have been thinking about it and I think we can make it work really well.
14:52 < yashsharan> Okay so here is where I am facing some confusion.I'm implementing mountainCartContinous environment and have written the code for it.But I saw that the rendering part of the encironment is dont by zoq_tcp_api.However I am not able to figure out where is the api being called in the mlpack repository
14:52 < yashsharan> *environment is done
14:57 < zoq> I guess, you wrote the mountain cart (continous) env similair to this one https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/reinforcement_learning/environment/cart_pole.hpp this is meant to be used for testing, in which case rendering isn't necessary.
14:57 < zoq> The tcp_api code uses the gym framework so adding new env's isn't necessary, you can just call: 'Environment env(host, port, environment);' to start the env, see https://github.com/zoq/gym_tcp_api/blob/master/cpp/example.cpp for an example.
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15:26 < yashsharan> Oh i get it now.Thanks for clearning my doubts.Also I was wondering why isnt gym_tcp_api already included in the mlpack codebase since ultimately if you want to run your RL agent you would need that api.
15:34 < zoq> That would add another dependency (boost asio), for a simple script you could also just link against.
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15:42 < yashsharan> Oh i see. Thanks.So ultimately if I want to train my Rl algorithms I would be doing it via gym_tcp_api right?
15:43 < zoq> right
15:45 < yashsharan> And the algorithms for reinforcemnt learning are written under methods/reinforcement learnings right?But when I lokked into the example file in the gym_api i saw that there isnt any algorithm being called from the reinforcement learning methods
15:46 < yashsharan> so if I want to train my RL again using a specific algorithm,say Double DQN,how will I call that method ?
15:47 < zoq> One option is to modify https://github.com/zoq/gym_tcp_api/blob/master/cpp/CMakeLists.txt and add mlpack as a dependency, and afterwards you can use the method inside https://github.com/zoq/gym_tcp_api/blob/master/cpp/example.cpp
15:48 < zoq> basically add 'find_package(MLPACK REQUIRED)
15:48 < zoq> '
15:50 < zoq> include_directories(${MLPACK_INCLUDE_DIR}) and add ${MLPACK_LIBRARY} to target_link_libraries(
15:50 < zoq> I can provide the modifications if you like
15:52 < yashsharan> That would be really helpful.Thanl you.
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15:56 < yashsharan> Also I made changes to the draft as suggested by you.So should I finally submit my proposal?
15:57 < zoq> That is up to you.
15:58 < zoq> https://gist.github.com/zoq/968f1ed2926200e9f1f1125c985e4d13
15:59 < yashsharan> Ok thank you.
16:01 < yashsharan> Also I had a suggestion. In the mlpack documentation it's not mentioned that to train an RL agent you would need gym_tcp_api.If that could be added to the documentation it would be helpful.
16:05 < zoq> Actually, it's not necessary, Gym is neat to run your RL method against some environments, but at the end, you define your own problem and use mlpack to solve it.
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16:08 < yashsharan_> Ohh yes seems you are right since gym isn't the only the option where people train there RL agents.
16:08 < yashsharan_> *thier
16:12 < zoq> Adding a section as an example is still a good idea.
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16:24 < yashsharan_> yeah that would be a good option
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16:29 < manthan> @rcurtin @zoq, i set the tolerance high for the GradientBatchNormTest(), as I manually calculated the values.
16:29 < manthan> can you let me know if you found something wrong with the implementation?
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16:33 < s1998_> zoq: I had submitted proposal for essential deep learning modules. Can you please take a look?
16:34 < manthan> @rcurtin, @zoq could you please have a look at my proposal draft that i have submitted.
16:47 < zoq> manthan: On my system, the gradient check failed about ~50% of the time with different random seeds. Also, the tolerance was quite high.
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16:54 < manthan> the tolerance you mean is tolerance for BatchNormTest right?
16:54 < zoq> right
16:55 < manthan> ya, thats because, I manually calculated the values for the passes using the formula and so there were precision problems
16:55 < zoq> I was talking about the CheckGradient tolerance (BOOST_REQUIRE_LE(CheckGradient(function), 1e-3);)
16:59 < manthan> ohh, ya i agree its more than 1e-4 that is used for other tests, but i think that is because changes to the parameters in this case will cause larger gradient shifts because we are essentially changing the shift and scale parameters.
17:00 < manthan> but i thought since 1e-3 is comparable, it shouldnt be a problem.
17:01 < manthan> However, i didnt try with random seeds and so would have missed something in that case
17:02 < zoq> So, even if we lower the tolerance once more, it shouldn't fail like 50% of the time.
17:03 < zoq> If you have the time, please feel free to recheck the backward step.
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17:09 < manthan> @zoq, sure i will go through it. Hope something interesting comes up :D
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17:20 < ckeshavabs> hello, I had a doubt regarding some of the implementation details used in DQN. Is there any module in mlpack that helps us find the clipped gradients after performing back-propagation? Because, I read that clipping gradients stabilises the learning process
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17:24 < ckeshavabs> I was looking for a module like - https://www.tensorflow.org/api_docs/python/tf/clip_by_norm
17:27 < zoq> ckeshavabs: Hello, http://www.mlpack.org/docs/mlpack-git/doxygen/classmlpack_1_1optimization_1_1GradientClipping.html
17:27 < zoq> here is an example: https://github.com/mlpack/mlpack/blob/master/src/mlpack/tests/gradient_clipping_test.cpp
17:28 < ckeshavabs> @zoq, thank you for the resources. I will check it out.
17:29 < zoq> Let me know if I should clarify anything.
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18:06 < Prabhat-IIT> zoq: you there?
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18:09 < zoq> Prabhat-IIT: yes
18:15 < Prabhat-IIT> zoq: Regarding the handling of constraints after much thought I've concluded that we can modify the existing functions to return the simple inequality contraints involving lower bound and upper bound of the feasible space. To handle custom mae contraints like x^2 + y^2<=1 the function should return True or False if custom contraints are satisfied.
18:15 < Prabhat-IIT> Now, How the function behaviour is clear to us.
18:16 < Prabhat-IIT> Then, further handling depends on the optimizer
18:17 < Prabhat-IIT> There can be many optimizers apart from PSO implemented in future for non linear contraint based optimization. Each can handle contraint in their own way
18:17 < zoq> Agreed, I had the same thought.
18:19 < Prabhat-IIT> For, eg. I've though that a simple way to handle contraints in PSO will be updating the particle position and velocity only if the particle's next position is in feasible space
18:19 < zoq> Prabhat-IIT: Yes, super simple to do.
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18:20 < Prabhat-IIT> zoq: So, what do you think should we use complex methods like this Tournament based approach https://cimat.repositorioinstitucional.mx/jspui/bitstream/1008/632/1/I-07-04.pdf which you've referred in mailing list or keep it simple?
18:22 < zoq> Prabhat-IIT: As for me, this sounds like that would cover a lot of problems, so for now I say let's go with the simple solution.
18:24 < Prabhat-IIT> zoq: Then I'll mention a simplistic approach in my proposal and would add the clause that the final method will be flexible and will be implemented only after thorough discussion with the mentors
18:25 < zoq> Prabhat-IIT: Sounds good, to me.
18:26 < Prabhat-IIT> zoq: One thing I'd like to ask is the Initialization phase. In PSO we initialize randomly. As far as upper bound and lower bound are concerned we can genrate a uniform random distribution within those bounds but the problem arise with custom made constraints like x^2+y^2<1
18:27 < Prabhat-IIT> If we try to generate initial positions randomly till all the positions are within this custom contraint there's no gaurantee at all that it will be
18:28 < Prabhat-IIT> zoq: So, do you have something in mind regarding this, how can we effectively handle initialization for custom contraints
18:29 < zoq> Yes, there is no gaurantee that it will converge, but a user could specify the maximum number of iterations. And a user could also provide initial parameters.
18:30 < Prabhat-IIT> zoq: Then it'll be all good to go :)
18:31 < zoq> Prabhat-IIT: Nice :)
18:32 < Prabhat-IIT> I hope we can turn our PSO implementation into a full fledged tool box suitable from noob to researcher :)
18:33 < Prabhat-IIT> zoq: one thing more, how'd you like the idea of that `PARTICLE` and `topologies`?
18:34 < Prabhat-IIT> I've introduced it just because there's so many variants based on different topologies and behaviour of Particles. If we stick to just one then it'll not be robust enough to be used in varied engineering applications
18:35 < Prabhat-IIT> These will allow anyone to implement even its own topology and particle easily with minimal efforts to suit his/her specific need
18:37 < zoq> Yeah, I get the idea, I think we could slightly simplify the classes, but it works as it is and could be used as a basis for a discussion.
18:39 < Prabhat-IIT> zoq: The classes will be more elegant and sychronized once we get into actual implementation. I'll also work in simplifying them.
18:40 < Prabhat-IIT> zoq: Also, How much technical details have to be included in the proposal. Too much technical details will make it messy I think
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18:41 < ckeshavabs> zoq: What exactly is the deliverables expected for the Double DQN implementation? From implementation perpspective, little changes need to be made to convert the existing DQN to Double DQN implemention as indicated in lines 133-137 here - https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp
18:42 < ckeshavabs> please correct me if I am wrong with any of my assumptions?
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18:58 < Nisha_> Hi @zoq, as suggested by you, i am working on my draft proposal for quasi rnns. Should i include the mathematical details ( regarding forget gates, input gates etc). too?
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18:59 < Nisha_> Also, which issues (related to this topic) can i solve to increase my chance of getting selected?
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19:14 < Atharva> I am facing a weird problem, on every execution of randu/n function of the arma library I get the same values, why is this happening?
19:14 < Atharva> Okay
19:14 < Atharva> Sorry
19:14 < Atharva> I forgot to set the seed
19:17 < rcurtin> :)
19:22 < zoq> ckeshavabs: Sounds about right, probably the first issue we have to solve is: https://github.com/mlpack/mlpack/pull/1091
19:24 < zoq> Nisha_: No need to go into the mathematical details, however, if you think there is something interesting you like to describe, please feel free to provide some more details.
19:25 < zoq> Nisha_: Ideally you can show that you get the overall idea.
19:26 < zoq> Nisha_: I'm not sure there is an open issue at this point, but you can always try to improve an existing method.
19:28 < zoq> Prabhat-IIT: Yeah, that is the tricky part, you can expect that any reviewer is kinda familiar with the topic.
19:30 < Nisha_> Okay, that sounds great. Will submit my draft proposal asap :)
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19:39 < rcurtin> I gave an hour-long talk today on machine learning, C++, and mlpack; maybe some of you might find the slides interesting: http://www.ratml.org/misc/mlpack_cb.pdf
19:40 < rcurtin> it's tough to gauge the feedback from the talk (I think it went well, the audience seemed to enjoy it), but one of the themes that I hear is that data scientists don't prefer to work in C++, so I think this emphasizes the importance of the Python bindings and bindings to other languages
19:40 < Atharva> rcurtin: I am planning to write a tutorial on vae in mlpack as a part of the project. Do you think it’s a good idea or should I use that time to implement more functionality?
19:41 < rcurtin> Atharva: personally I think documentation is extremely important---you can write the best code in the world, but if nobody knows how to use it, it will never see the light of day :)
19:43 < Atharva> rcurtin: yeah, even I would want that code to be an integral part of the library which people know how to use. So I will include a tutorial. :)
19:43 < rcurtin> sounds good
19:44 < Atharva> Also, I have also put a CLI/python binding for the vae class as one of my objectives.
19:47 < zoq> rcurtin: Looks really, interesting, not sure I get the image reference on page 19
19:48 < yashsharan> I was wondering is it possible to have gpu support for mlpack ,maybe possibly in the future?
19:50 < zoq> yashsharan: Currently, you could build against NVBLAS, and in the future Bandicoot (GPU accelerator add-on for Armadillo) will help.
19:51 < rcurtin> zoq: it's the cake from https://www.youtube.com/watch?v=gAYL5H46QnQ
19:53 < zoq> rcurtin: ohh
19:54 < yashsharan> ohh nice.Also i'm stuck at interegrating appveyor CI issue in the models repository.I have mentioned the error in the pull request.If you could have a look at it that would be great.Thanks.https://github.com/mlpack/models/pull/10
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19:58 < zoq> "This will _definitely_ get me best paper at ICML! I can // feel it!" :)
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20:44 < Nisha_> Great slides @rcurtin. Found it very interesting. As you mentioned, binding to Python or other languages is extremely important. I had a doubt regarding this. How exactly do we go about implementing python binding in mlpack? Like will it be specific to a particular class? If so, I would like to include this too :)
20:44 < Nisha_> I am sorry if my doubt is naive
20:45 < zoq> Nisha_: The python bindings are based on the cli's: http://www.mlpack.org/docs/mlpack-git/doxygen/bindings.html
20:48 < Nisha_> Thank you :)
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21:00 < rcurtin> Nisha_: yeah, basically we use an automatic binding generator to provide the exact same interface for Python, the command-line, and other languages (although nothing else is implemented yet)
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21:27 < Arshdeep> I want to solve issues in string processing utilities, guide me where i can find them.
21:28 < rcurtin> Arshdeep: there are currently no open issues for the string processing utilities project
21:29 < rcurtin> since the functionality is new, there is nothing related to be solved at this time
21:31 < Arshdeep> thanks, also tell me how to approach string processing utilities as i want to work on it
21:37 < rcurtin> https://github.com/mlpack/mlpack/wiki/Google-Summer-of-Code-Application-Guide should be helpful
21:37 < Arshdeep> thanks
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