Nice work Sebastian! I agree, Hierarchy as a concept is very important to neocortex emulation, and to my knowledge Numenta has not released and is still working on an implementation of this feature. The make-shift "hierarchies" that can be composed using repeated combinations of the current algorithms are not "true" hierarchies in the HTM sense (ascending regional constructs). This being the case, for the time being repeated layers may not be as useful (depending on your application of course), as they will be when the hierarchy is actually part of the algorithm.
Cheers, David On Wed, Jan 13, 2016 at 11:35 AM, Sebastián Narváez <[email protected]> wrote: > I had the same problem as you, and implemented a Layer class to play easly > with different combinations of the Nupic modules (encoders, sps and tms). > You can check it out here: > > https://github.com/larvasapiens/htm-teul/tree/master/Learning > > However, from my experiments, a one-level network > (encoder->sp->tm->classifier) works better than a multi-level hierarchical > approach with the current implementation of nupic. Or maybe I didn't used > the correct parameters for my experiments. > > On Wed, Jan 13, 2016 at 11:43 AM, cogmission (David Ray) < > [email protected]> wrote: > >> Hi Wakan, >> >> I while back a worked up a very raw example of connecting the algorithms >> (in a very rough manner), in Python. Maybe this might be of some help? >> (see attached) >> >> Cheers, >> David >> >> On Wed, Jan 13, 2016 at 8:59 AM, 박진만 <[email protected]> wrote: >> >>> I did not use Network API. I made the structure by combining some >>> example codes like hello_sp.py and hello_tm.py in >>> https://github.com/numenta/nupic/tree/master/examples >>> >>> I made my midi encoder by combining 3 scalar encoders and 1 category >>> encoder. I used FastCLAClassifier in nupic.bindings.algorithms >>> >>> I want to make an hierarchical structure of HTM, not using network API, >>> but using those raw simple codes. >>> >>> Thank you. >>> >>> 2016-01-13 23:35 GMT+09:00 Wakan Tanka <[email protected]>: >>> >>>> Hello, >>>> May I ask which framework are you using? OPF or Network API, from what >>>> you've typed I guess this is Network API. I'm wondering how you made: >>>> raw midi data -> Encoder -> Spatial Pooler -> Temporal Pooler -> CLA > >>>> Classifier -> prediction >>>> Also what encoder did you use? Have you followed some example codes? >>>> >>>> Thank you >>>> >>>> On 01/13/2016 02:47 PM, 박진만 wrote: >>>> >>>>> Hello, I'm newbie to NUPIC& NUPIC-mailing list. >>>>> >>>>> I'm working on training midi files(*.mid , a sort of music file) using >>>>> low-level codes. >>>>> >>>>> Low-level codes mean just a raw sp and tp code, not network API or OPF. >>>>> I prefer to use low-level codes because it's easier for me to modify >>>>> the >>>>> codes. >>>>> I used a simple structure like this: >>>>> raw midi data -> Encoder -> Spatial Pooler -> Temporal Pooler -> CLA >>>>> Classifier -> prediction >>>>> The result was quit awesome. the HTM successfully predicted the whole >>>>> sequence with no error. >>>>> >>>>> Then I wanted to change the structure to be hierarchical like this : >>>>> raw midi data -> Encoder -> SP1 -> TP1 -> SP2 -> TP2 -> CLA Classifier >>>>> -> prediction >>>>> >>>>> but I cannot implement the structure because i don't know how the layer >>>>> 1 and layer 2 is linked. I already watched "hierarchy_network_demo.py", >>>>> but the code just tells us "UniformLink". >>>>> What does the term "UniformLink" mean? >>>>> >>>>> I think it's gonna be a strange architecture if TP1's output(array of >>>>> cells) becomes the input of SP2, because in this hierarchy, layer 2 >>>>> (SP2 >>>>> and TP2) will have bigger column dimension than those of layer 1, which >>>>> is somehow weird. >>>>> >>>>> to sum up, my questions are : >>>>> >>>>> 1. How they are linked between layers. >>>>> 2. Any hierarchy structure examples in low-level (not Network API, >>>>> not OPF) >>>>> >>>>> Any comments would be very helpful. >>>>> >>>>> Thank you. >>>>> >>>>> >>>> >>>> >>> >> >> >> -- >> *With kind regards,* >> >> David Ray >> Java Solutions Architect >> >> *Cortical.io <http://cortical.io/>* >> Sponsor of: HTM.java <https://github.com/numenta/htm.java> >> >> [email protected] >> http://cortical.io >> > > -- *With kind regards,* David Ray Java Solutions Architect *Cortical.io <http://cortical.io/>* Sponsor of: HTM.java <https://github.com/numenta/htm.java> [email protected] http://cortical.io
