Hi An, Please see [1]. It gets 95.5% accuracy. However, please note this is a very simplistic system (just SP+KNN). It does not incorporate hierarchy, temporal pooling, or any sort of learning of invariances. (BTW, anything less than 99% is not considered very good for MNIST. MNIST is all about getting those last few corner cases! :-)
--Subutai [1] https://github.com/numenta/nupic.research/tree/master/image_test On Sat, Jan 17, 2015 at 11:00 PM, <[email protected]> wrote: > Hello. > > Sorry for the last email. Thx to the rich formatting :( ... I have to type > again. > > Recently, I got the result of the test. I followed the source code and > built the Spatial Pooler + KNN classifier. Then I extracted images from > MNIST dataset(Train/test : 60000/10000) and parsed them to the model. I > tried to test with different parameters (using small dataset: Train/Test - > 6000/1000 ), the best recognition result is about 87.6%. After that, i > tried the full size MNIST dataset, the result is 89.6%. Currently, this is > the best result I got. > > Here is the statistics. It shows the error counts for each digits. the Row > presents the input digit. the column presents the recognition result. Most > of the "7" are recognized as "9". It seems the SDR from SP is still not > good enough for the classifier. > > I found some interesting things. When I let the "inputDimensions" and > "columnDimensions" be "784" and "1024", the result will be around 68%. If i > use "(28,28)","(32,32)" and keep others the same, the result will be around > 82%. That 's a lot of difference. It seems the array shape will effect SP a > lot. > > Did any one get a better result? Does any one have some suggestion about > the parameters or others? > > Thank you. > An Qi > Tokyo University of Agriculture and Technology - Nakagawa Laboratory > 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588 > [email protected] >
