Hi Subutai, The training and testing data sets are the same. I tested 4 different configurations and all configurations achieve perfect results for the data sets that contain between 1 and 13 characters. Two configurations get perfect results for the data set with 14 characters and one achieves it for 15 and 16 characters. Are you saying you think it should be perfect for larger data sets?
On Tue, Jul 8, 2014 at 9:52 AM, Subutai Ahmad <[email protected]> wrote: > 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 > > > > _______________________________________________ > 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
