I think there are three phases for doing this problem with NuPIC and each subsequent one will yield much better results.
1. Just use the classifier. This isn't what the classifier is intended for and won't work very well. 2. Use the spatial pooler to learn invariant spatial representations and the classifier to turn the SP output into a classification of which character is in the image. 3. Add temporal pooling and hierarchy. This wasn't intuitive to me but is important for doing well at image recognition problems. The basic idea is that you translate the training images and feed the moving characters into the CLA (with SP, TP, and classifier). This learns the transitions of the training characters as they move around in the input field or are stretched or otherwise manipulated. With temporal pooling, higher levels in the hierarchy will form stable representations of the character as it moves around and is translated. During the testing phase, show it a single static image and it should activate the stable representation of the character at the higher levels of the hierarchy and the classifier will have learned that that representation matches the correct character. I haven't done these things but I would recommend putting your code somewhere that we can collaborate to get the parameters right. I would start with 2048 columns as Marek suggests (this has to match in both SP and TP). I don't think 8 gigs memory should be an issue. You may also want to use topology but I am not sure if that is necessary. On Wed, Jan 22, 2014 at 10:50 AM, Marek Otahal <[email protected]> wrote: > Hi, > > text follows below.. > > > On Wed, Jan 22, 2014 at 7:29 PM, Allan Inocêncio de Souza Costa < > [email protected]> wrote: > >> Of course! >> >> Following is my model_params. My memory can handle about 512 columns in >> SP. Also, the pixels fields are setted in the end: >> >> MODEL_PARAMS = { >> # Type of model that the rest of these parameters apply to. >> 'model': "CLA", >> >> >> 'spEnable': True, >> > > So you are using spatial pooler actually. > > >> >> 'spParams': { >> # SP diagnostic output verbosity control; >> # 0: silent; >=1: some info; >=2: more info; >> 'spVerbosity' : 0, >> >> 'globalInhibition': 1, >> >> # Number of cell columns in the cortical region (same number >> for >> # SP and TP) >> # (see also tpNCellsPerCol) >> 'columnCount': 256, >> > > too low number I think, make it the default 2048, or even more. > >> >> 'inputWidth': 256, >> > Is this wrong? The input is much bigger (28x28x121) > > Missing here, but for speed use the cpp implementation (looks like you're > using a model_params file as a template, but an old one? Check hotgym > example. > > >> # SP inhibition control (absolute value); >> # Maximum number of active columns in the SP region's output >> (when >> # there are more, the weaker ones are suppressed) >> 'numActivePerInhArea': 40, >> >> 'seed': 1956, >> >> # coincInputPoolPct >> # What percent of the columns's receptive field is available >> # for potential synapses. At initialization time, we will >> # choose coincInputPoolPct * (2*coincInputRadius+1)^2 >> 'coincInputPoolPct': 0.5, >> >> # The default connected threshold. Any synapse whose >> # permanence value is above the connected threshold is >> # a "connected synapse", meaning it can contribute to the >> # cell's firing. Typical value is 0.10. Cells whose activity >> # level before inhibition falls below minDutyCycleBeforeInh >> # will have their own internal synPermConnectedCell >> # threshold set below this default value. >> # (This concept applies to both SP and TP and so 'cells' >> # is correct here as opposed to 'columns') >> 'synPermConnected': 0.1, >> >> 'synPermActiveInc': 0.1, >> >> 'synPermInactiveDec': 0.01, >> >> 'randomSP': 0, >> }, >> >> # Controls whether TP is enabled or disabled; >> # TP is necessary for making temporal predictions, such as >> predicting >> # the next inputs. Without TP, the model is only capable of >> # reconstructing missing sensor inputs (via SP). >> 'tpEnable' : False, >> >> >> >> 'clParams': { >> 'regionName' : 'CLAClassifierRegion', >> >> # Classifier diagnostic output verbosity control; >> # 0: silent; [1..6]: increasing levels of verbosity >> 'clVerbosity' : 0, >> >> # This controls how fast the classifier learns/forgets. >> Higher values >> # make it adapt faster and forget older patterns faster. >> 'alpha': 0.001, >> >> # This is set after the call to updateConfigFromSubConfig and >> is >> # computed from the aggregationInfo and predictAheadTime. >> 'steps': '0', >> }, >> >> 'anomalyParams': { >> u'anomalyCacheRecords': None, >> u'autoDetectThreshold': None, >> u'autoDetectWaitRecords': None >> }, >> >> 'trainSPNetOnlyIfRequested': False, >> } >> } >> >> for i in range(0,784): >> > > this does work? I thought this is just a json-like file? > > >> MODEL_PARAMS['modelParams']['sensorParams']['encoders']['pixel%d' % >> i] = { >> 'fieldname': u'pixel%d' % i, >> 'n': 121, >> > I'm surprised yous truggle to fot this in RAM, I;ve done experiments with > 100k wide encoded vectors. Try the cpp implementation. > >> 'name': u'pixel%d' % i, >> 'type': 'ScalarEncoder', >> 'minval':0, >> 'maxval':255, >> > Also, preprocessing just to b/w could help (?) > >> 'w': 21} >> >> >> > Cheers, Mark > > -- > Marek Otahal :o) > > _______________________________________________ > 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
