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)
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