Hi Jim, Thanks for doing this experiment. I have one quick question: are the training and test sets the same? If so, I think the SP should perform perfectly in this task.
Thanks, --Subutai On Sun, Jul 6, 2014 at 10:43 PM, Jim Bridgewater <[email protected]> wrote: > Hi Yuwei, > > Thanks for the feedback, I'm glad you found it interesting. > > I don't know why the training for the 3 character data set takes so > long, but it just occurred to me that it may have something to do with > the particular characters involved. The one character data set is an > image of a zero, 0. The two character data set contains images of 0 > and 1 which are pretty easy to distinguish from one another. The > three character data set contains images of 0,1, and 2. The 0 and > the 2 are much harder to distinguish from one another, in fact when I > ran Ian's spviewer demo on a hexadecimal data set (0-9, A-F), the > column that represents 2 is the last one to stabilize. > > Just tried a three character data set consisting of O, X, and I and it > only took 2 cycles to learn it so it does appear to be related to the > characters involved, but I'm still not sure why the number of training > cycles decreased for the data sets containing between 4 and 13 > characters because the 2 and 0 are still included. > > By no SDR collisions I mean that two different characters cannot have > the exact same SDR, but their SDRs can share bits as long as the two > SDRs are different by at least one bit. > > I put "plot accuracy as a function of training cycles" on my ToDo > list. I'll let you know when I check that one off. > > > > > > > On Sun, Jul 6, 2014 at 2:29 PM, Yuwei Cui <[email protected]> wrote: > > Hi Jim, > > > > This is an interesting task. I am confused about how the amount of > required > > training varies with the data set size (Fig. 3). Do you have any > intuition > > on this (why it first increase and then decreases). I am also having > trouble > > to understand the criterion for stop training sounds. You said there > must be > > "no collisions" for images of different characters. But for SDR, a small > > number of collisions should not affect the performance much (since the > > representation is distributed). It would be interesting if you could plot > > the "error rate" as a function of iteration number during learning. > > > > Yuwei > > > > > > On Sun, Jul 6, 2014 at 12:20 AM, Jim Bridgewater <[email protected]> > wrote: > >> > >> Hi everyone, > >> > >> I wanted to make sure I know how to size the spatial pooler properly > >> for a given task so I ran a few tests to measure its image recognition > >> accuracy on data sets of different sizes. One of the surprising > >> results was how the amount of training required varies with the size > >> of the data set. The results are shown in the attached pdf. > >> > >> -- > >> Jim Bridgewater, PhD > >> Arizona State University > >> 480-227-9592 > >> > >> _______________________________________________ > >> nupic mailing list > >> [email protected] > >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >> > > > > > > > > -- > > -- > > Yuwei Cui > > > > Algorithm Internship, Numenta Inc. > > > > PhD Candidate, Neuroscience and Cognitive Science > > > > University of Maryland, College Park, MD, 20742 > > > > Homepage: http://terpconnect.umd.edu/~ywcui/ > > > > > > _______________________________________________ > > 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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