So, the spatial pooler doesn't offer spatial invariance because it's pooling together "similar" inputs where similar means having overlapping bits...right? Therefore an image moved over by 1 pixel wouldn't have any overlapping bits with its original, so it would look like a totally novel input to the SP.
On Wed, Mar 5, 2014 at 3:05 PM, Ian Danforth <[email protected]>wrote: > 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 >> >> > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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