[Top][All Lists]

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: Working on nnet package

From: Alois Schlögl
Subject: Re: Working on nnet package
Date: Fri, 12 Apr 2019 00:42:09 +0200
User-agent: Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Thunderbird/60.6.1

On 4/10/19 8:34 PM, Evangelos Rozos wrote:
> Will anyone advice me what should I do to become the maintainer of
> this package?
Disclaimer, I'm not a maintainer of Octave or Octave-forge; so I'm not
in the position to give you an authoritative answer to that. However, I
can contribute my own thoughts on this.

Concern your question, I'd answer this in the following way: As a
maintainer, you need to be able to deliver. In this case, deliver an
improved version of nnet, that is somehow *useful to others*.

Concerning the topic of ANN, machine learning, etc., there are a few
things to consider. As maintainer of the NaN-toolbox (A statistics and
*machine learning* toolbox ...), I do have a few thoughts on that topic,
which you might consider or ignore. You should consider that between
2010 and today, there was a big shift in ML, that is "Deep Learning"
(DL). DL uses Neural Networks of course, but nowadays, there are
powerful DL frameworks around. IMHO, I'd give it a second thought
whether you really want to re-implement ANN's in order to set up just
another DL framework. The level of sophistication in these frameworks is
very high, any new implementation can hardly compete there. Moreover,
you might also want to exchange (pre-trained) Deep learning models with
other frameworks.

A much more promising approach seems incorporating some open sourced
Deep Learning framework into Octave. E.g. setting up an interface to
Tensorflow or ONNX through mex/oct C/C++ interface would be an
interesting task. Looking at the c_api of TF 1.13, it seems that one
need to get familiar with dataflow graphs, and protobuf, etc. Matlab
uses ONNX, so for compatibility one might use ONNX; otoh, Google's
Tensorflow seems to be technologically more advanced, therefore, I'd go
with a mex interface to tensorflow. It's certainly not a trivial
project, but it should be possible. There are already TF interfaces for
a number of other languages, so way not Octave as well.



P.S.: Some experimental code for a mex-interface to Tensorflow is
available here:


Feel free to continue from here. 

reply via email to

[Prev in Thread] Current Thread [Next in Thread]