Yes, that is the size I used to get those results. Note that this version is very simplistic - it has no temporal pooling and does not learn invariances, so it has to memorize a lot of stuff.
--Subutai On Sat, Sep 26, 2015 at 12:41 AM, <[email protected]> wrote: > > "numColumns = 12288". From 64x64 to 12288, Is it 1 > dimension? and btw. this's 3 times than before. a really big one. > > > On Tue, 22 Sep 2015 20:33:20 -0700 > Subutai Ahmad <[email protected]> wrote: > >> If you use increment and decrement of 0.0, you are essentially using a >> randomly initialized SpatialPooler. It turns out that such a "random SP" >> is >> actually pretty decent. You will get reasonable SDRs out of it. Training >> will make the SP more resistant to noise. >> >> For MNIST the difference in test accuracy between trained and untrained SP >> is not large. However it takes a lot longer to train, so I left it out of >> the code. If you want to use a trained SP, you can try the parameters >> below. However you will need to go through the training set 3 or 4 times. >> >> Separately, I have verified that if you train the SP, the network is much >> more robust to random noise than an untrained SP. This is different from >> the normal MNIST testing protocol. >> >> --Subutai >> >> numInputs = 1024 >> numColumns = 12288 >> numActiveColumnsPerInhArea = 1600 >> potentialPct = 0.4 >> globalInhibition = 1 >> stimulusThreshold = 0 >> synPermActiveInc = 0.001 >> synPermInactiveDec = 0.0005 >> synPermConnected = 0.5 >> minPctOverlapDutyCycles = 0.001 >> minPctActiveDutyCycles = 0.001 >> dutyCyclePeriod = 1000 >> maxBoost = 3 >> CPP SP seed = 1956 >> >> On Tue, Sep 22, 2015 at 7:55 PM, [email protected] <[email protected]> >> wrote: >> >> Hello, Nupic >>> Recently, I test nupic.vision project, the result is good but I got one >>> question. >>> As we know, the connect value between synapses will be update when we >>> thain HTM newwork. How do we update the connected value depend on two >>> parameters: "synPermActiveInc" and "synPermInactiveDec". am I right? >>> But in run_mnist_experiment.py example,these two parameters is 0. That's >>> really strange. if we set these parameters to 0, how to traing? it's i >>> llogical. >>> is anyone have any explanation or reference material about this >>> Experiment? >>> Thank You. >>> Cyan >>> ------------------------------ >>> [email protected] >>> >>> > >
