A gentle reminder, as you may have missed the previous email. On Sun, Mar 27, 2022 at 1:13 PM Shubham Agrawal <[email protected]> wrote:
> Hello Marcus, > > By breaking API, I meant both old and new API as I am thinking about > defining the link between layers. > Example - > Layer2 = Layer2Type(..PrevLayerRequired) > Something on this line. So that people do not need to know complex API for > making links between layers as it might be confusing for many people. I > have thought about giving backward compatibility, and it probably will be > easy to give backward compatibility as every ANN created earlier comes > under DAG. > I will convert all std::vector to std::map in multi_layer.hpp file < > https://github.com/shubham1206agra/mlpack/blob/ann-vtable/src/mlpack/methods/ann/layer/multi_layer.hpp> > so that I can convert everything to DAG structure. I am thinking of > storing graph edges in as adjacency lists format. > > About the 2nd project, let us implement most of the models as in Pytorch. > All models only for a forward pass don't take much memory. When GPU support > comes, it will be helpful if we can give more variety of models > architecture. And I will look for non-implemented layers too. > > Regards > Shubham Agrawal > > On Sat, Mar 26, 2022 at 8:56 PM Marcus Edel <[email protected]> > wrote: > >> Hello Shubham, >> >> Thanks for your interest in the project. I think you are aware that Ryan >> is currently refactoring the ann codebase. >> So when you say "This project will introduce breaking changes in ANN >> API." is that based on the current API or >> the updated API, from the table PR? There was already some discussion on >> the mailing list about the DAG project: >> >> http://knife.lugatgt.org/pipermail/mlpack/2022-March/004648.html >> >> In case you haven't seen it already. It should be possible to implement >> DAG without breaking the new API, >> especially since it introdcues some new concepts, like the MultiLayer >> which we can build on. >> >> I like the second idea as well, one thing you should keep in mind that we >> currently don't have GPU support >> (will change soonish), that is why we focused in the past on smaller >> models that you can run on a resource >> constrained device, mobilenet is one example. So in your proposal I would >> go through the list and select reasonable >> models, also, you should check if one of the models require the >> implemenation of a non implemented operation. >> >> I hope anything I said was helpful, let me know if I should clarify >> anything further. >> >> Thanks >> Marcus >> >> >> > On Mar 15, 2022, at 12:09 AM, Shubham Agrawal < >> [email protected]> wrote: >> > >> > Hello everyone >> > >> > I hope this email finds you well. My name is Shubham Agrawal. I am >> currently doing my undergrad studies (3rd year) at IITD, India. I have been >> contributing to mlpack organization for the past 2 to 3 months, and I want >> to participate in GSoC '22 under this organization. I wanted to propose two >> large projects (~350 hours), and I wish to work on at least one of the >> ideas. Or any other idea can work too if there is some issue with my >> proposal. >> > >> > I am attaching a pdf document that contains my proposal. I hope to get >> valuable feedback from the community for building my proposal with this >> thread. >> > >> > I am looking forward to hearing from you. >> > >> > Regards >> > Shubham Agrawal >> > GitHub username - shubham1206agra >> > <gsoc_proposal_draft.pdf>_______________________________________________ >> > mlpack mailing list >> > [email protected] >> > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> >>
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