On Fri, Jul 4, 2014 at 9:31 PM, Jim Bridgewater <[email protected]> wrote:
> Hi Ian, > > You are correct. Thanks for the feedback, I've updated the > introduction and attached the revised document. > > There is still the question of whether these results will hold with > more active columns and I plan to run that experiment and post the > results soon. I am planning to use your Amazon EC2 image for this > since these jobs can take some time for large numbers of columns. > > Awesome! Let me know if you end up installing tools that you think should be part of the default AMI. > For pooling, I want to duplicate the characters in the data set using > different fonts and see if I can get the spatial pooler to generalize > between the different fonts. > > > Looking forward to those results! Ian > > On Fri, Jul 4, 2014 at 6:43 PM, Ian Danforth <[email protected]> > wrote: > > Jim, > > > > To be clear you have 186 training examples and 256 columns, 100% > potential > > pool and you have 1 column on at a time, so you would expect 100% > accuracy. > > Correct? You might want to note this up front. Something like "this > should > > be an easy task for the spatial pooler, however certain parameter > > configurations were found to be problematic." > > > > Also I know you know this but for others reading along, to do "pooling" > the > > number of training examples needs to be at least > than number of columns > > and preferably >>. Otherwise each column will perfectly fit a single > > example. Also you need to test on a separate set of data to see how well > the > > model generalizes. > > > > Ian > > > > > > On Fri, Jul 4, 2014 at 12:29 AM, Jim Bridgewater <[email protected]> > wrote: > >> > >> Hi everyone, > >> > >> I'm working on a NuPIC vision toolkit for the Season of NuPIC this > >> summer and I've created a GitHub repo for it called nupic.vision. The > >> URL is: > >> > >> https://github.com/baroobob/nupic.vision > >> > >> There is a demo for those who want to try it out. > >> > >> I've attached the write up of results from using this toolkit to > >> investigate the effects of the synapse connection threshold, > >> synPermConnected, the permanence increment for active synapses, > >> synPermActiveInc, and the permanence decrement for inactive synapses, > >> synPermInactiveDec, on image recognition accuracy and the amount of > >> training required. > >> > >> -- > >> Jim Bridgewater, PhD > >> Arizona State University > >> 480-227-9592 > >> > >> _______________________________________________ > >> nupic mailing list > >> [email protected] > >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >> > > > > > > _______________________________________________ > > nupic mailing list > > [email protected] > > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > > > > > > -- > Jim Bridgewater, PhD > Arizona State University > 480-227-9592 > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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