You can get it from training data as well you just need a lot of it. You can also get invariance with hierarchy and convolutional training. On Mar 5, 2014 1:02 PM, "Chetan Surpur" <[email protected]> wrote:
> Does general spatial invariance come as a result of temporal pooling + > saccades? If not, how would the CLA achieve it? > > > On Tue, Mar 4, 2014 at 1:03 PM, Subutai Ahmad <[email protected]> wrote: > >> Hi Traun, >> >> Your basic approach seems quite reasonable as a first step. I would >> definitely look at Kevin's tutorial in the link that Matt sent out as it >> might answer many of your questions about the class. (And don't use the >> oldpy implementation. :-) >> >> For doing classification, take a look at: >> >> py/nupic/algorithms/KNNClassifier.py >> >> There are some examples of using it here: >> >> tests/integration/py2/nupic/algorithms/knn_classifier_test/ >> >> It is lower level than the CLAClassifierRegion. For distance metrics, >> with SDRs we usually use an overlap distance metric (count the number of >> bits that are common) rather than Euclidean distance. If you are just using >> a spatial pooler it shouldn't matter too much, but it does matter a lot >> once you start using the temporal pooler. >> >> I think your assumptions about close matches are reasonable. You might >> not get an exact match unless the inputs are really really close. In >> particular, the spatial pooler won't do too well at learning general >> invariances (and it's not supposed to). For example, if you shift the image >> by one or two pixels you might get a very different output SDR. >> >> I expect you will have to understand and tune some of the parameters. I >> think you'll learn a lot in the process. >> >> Look forward to hearing more as you work on this! >> >> --Subutai >> >> >> >> On Tue, Mar 4, 2014 at 11:05 AM, Matthew Taylor <[email protected]> wrote: >> >>> Take a look at the links in this wiki we just created: >>> https://github.com/numenta/nupic/wiki/Introduction-to-the-Algorithms >>> >>> Kevin is working on a tutorial that might help you use the SP. >>> >>> --------- >>> Matt Taylor >>> OS Community Flag-Bearer >>> Numenta >>> >>> >>> On Mon, Mar 3, 2014 at 6:15 PM, Traun Leyden <[email protected]>wrote: >>> >>>> >>>> I'm trying to figure out how to write a digit recognizer implementation >>>> using a spatial pooler and had a few questions. >>>> >>>> The data would be similar to mnist, but I would start with a much >>>> simpler data set. The input vector would be a 1d vector of 784 input >>>> elements that represented the 2d image array of 28x28 pixels. As a >>>> simplification, the elements of the vectors would be 0 or 1 (as opposed to >>>> greyscale values used in mnist). >>>> >>>> The data sets I was planning to use: >>>> >>>> * For training (online learning), I would create ideal versions of the >>>> 0-9 digits >>>> >>>> * For testing (eg, online learning turned off), I would create noisy >>>> versions of those same digits >>>> >>>> >>>> The approach I was planning to take: >>>> >>>> 1) Create a spatial pooler instance >>>> >>>> 2) Turn on online learning >>>> >>>> 3) Repeatedly present ideal input vectors of 0-9 to the spatial pooler >>>> >>>> 4) Record the final SDR's of each input vector >>>> >>>> 5) Turn off online learning >>>> >>>> 6) Present noisy input vectors of 0-9 to the spatial pooler >>>> >>>> 7) Find the "closest" SDR recorded in step 4, and consider that the >>>> inferred SDR of the spatial pooler. I would compare the inferred SDR with >>>> a known expected SDR, which would be used to calculate the overall error. >>>> >>>> >>>> Does that sound like a reasonable approach? If not, what's a >>>> recommended approach to go about building a simple digit recognizer using >>>> the spatial pooler? >>>> >>>> Also I had some specific questions about how to use a spatial pooler: >>>> >>>> * Are there any examples that directly use a spatial pooler that I can >>>> look at? The closest I could find was >>>> https://github.com/allanino/nupic-classifier-mnist.git, but that uses >>>> OPF and I want to go straight to the lower level code and use the spatial >>>> pooler. >>>> >>>> * After looking at the code for the spatial pooler implementations, the >>>> "py" and "cpp" implementations don't seem to return any values. Only the >>>> "oldpy" implementation seemed to return a list of the active columns. >>>> Here's the method signature of the cpp spatial pooler I'm looking at in >>>> spatial_pooler.cpp: void SpatialPooler::compute(UInt inputArray[], bool >>>> learn, UInt activeArray[]) { } If they don't return any values, how are >>>> they supposed to be used? >>>> >>>> * Is there anything special to do with regards to recognizing 2d image >>>> patterns? Or will the columns naturally form 2d receptive fields of the >>>> input vector? >>>> >>>> * Since I want to basically classify digits, should I be looking at the >>>> CLAClassifierRegion for guidance? The idea I came up with for comparing >>>> the SDR's feels a bit janky, and it seems like there should be a cleaner >>>> way. Eg, something helps take care of the classification work but that's >>>> still lower level than the OPF? >>>> >>>> * I am expecting to see exact matches for SDR's when presenting input >>>> vectors that only have a little bit of noise, and "close" matches for input >>>> vectors that have a lot of noise. Is this a correct assumption? Is there >>>> any built-in machinery for measuring how close two SDR's are to each other? >>>> >>>> >>>> Thanks in advance for any help, I'm really excited to get started >>>> building this. I'm planning to contribute it back in a pull request once >>>> it's working. >>>> >>>> >>>> >>>> _______________________________________________ >>>> 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 >>> >>> >> >> _______________________________________________ >> 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 > >
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