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 taXpp0117pFyd4jUsxBlzq1t9+aFCcWRhbZvdMO8BF9d3SpWaG+SJmWVv3UorfS5sfOoTon32SoZuXU0vbYZhaI0drh7nxQJ2zpA3zJ+551a3Bh5+tFAcOXftZ94js7tdWNsefGzUhpfdc3rOaBpMu8MH135K65aVMxbXdw4deTEMcxTbvr//h9mV7xmvXtQz2tDEzHIQ23hRg50z3ETiP2dQUgyCoFSpLa7vLKxtF4ojjBHuKc5oeurnVrf688NutAeLo2mizZi5J2aW3Zwtkbl5OWNydkXeMGfmXlzfKY3VyLP/nEEZOgiCo2c/vXj9j/L4iTAMC/sPMNpOwxmL6ztPjr1wJ5hJriI7eMUZu95yB8vV81//Ojm7ksvt40UN1DPa0PFzX86tbtl/UlLsyw+5L6YNHytnUJ4OguCNxfVSpeY5Z1Be5I2nnNvuqONc2PTUO9H+MXO3ccX/1vrzw9667ZZhtsxNTBkEwT9f+KY0VvOcMwiJ+vPDs5/9YF+cnF0JwxxhB8u7dMwZVGgJw5CCOVgcjQRzYW37kcefCMPw5PmvaQevOIPA4ui7n9hInr50I5fbR9jB8haoZ3Que784vbTJFj5uznjlnY+tVc85g7iNcZRE1G13lIYzGp96SoS80ZbreKBMEwTBqaUN/90ap1rAapiTM149c/ni9T/Ip+ecQYm8VKldWP/dvkgVDsaKURrOsMF8cuyFRM6I7OAPZyRaomEUFp8k1DNSnSH2+2/2cRPXquecMTGzLFrM4HUb73ugV3jH0dLnwnqnfjfabHfbLG4bfYoxf6sv3TLM35+hgjMIKQ6Wx13OIPhgdJ6+P6MeZ8R38IczqNZysDweKV1Uaif788NcQyeoZ6Q6Q4XiCG/y7mXOKFenrMPppU3P3Zrdfh0qCQhFWI4z7kT7q1/uRvurXzJ3W09U52cx2QW3YoZ7lDMS6xmUs8EZKfXe51sDhf2Hq8cjJ4sGU966+B3L 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 MrcutAWdICvUMvwTOgFtXunIh3Aob1pS5dbk14AxJoZ7hl8AZcOtKVy6EW2HDmjK3LrcGnCEp1DP8EjgDbl3pyoVwK2xYU+bW5daAMySFekZ7WljbLhRH3BVIppc2GY8vwRmL6zulSs31PDGzzGhYV3jT/Pr00qbrrVSpLa7v2J/OrW715YciC9QUiiMLa9sdv2PKXJhw6s9eaWuHbrpN9j/GZo/dLdkLw9C1Z485vbTp/uhgefzC+u/pDDNn7sb+U4eX2e3C2vbgY6PuxXDKXGe83nqQM977fGugsL9QHPng2k/19jl96UYuty8IgqPvftJxtFHPaEM2kVDys1mHMReyc4b1TEmREnm5OsVoWFd4O/7diZnlIAj68kNzq1vWmxvJRM6IsEi7SpML46d+sDgaMdyfH26wQzfdJvq39i6s/27t8SXCVG6tPQKIiL3J2ZUwDPvyQ3OrP1rgOFw9nuYdeTP33OrWwMOP3uP/sdGUDrvgltji9KUbYZjjRY2e4ozF9Z0nx16gGBb2H6jHGYvrO4eOvEi7peEM1DPaEGUaN+3RVzlv2mY0THerlBqFIqwuvB3/7pt//Te3MkHE5saW3PIWYDrOhU1PvbPDj5m7TbY3xmyP0W1Te/GrZbA4mvLjMGZu63/2sx/EwsvJGZOzK2GYc6mCyIMRjHqHMxbWth95/IkwDE+e//rJsRcGi6P1OGNydmXg4UdfeefjXG4f6hl1xcsZ5epU5Ivbc84ge4yjJImGdYWX61DxRO4VZzQ99XQ7znttMHLGHXt8oyS8bq29Fo/Agk2Mmbtd/9m6NRu3y+Mn+vPDLhWBM9JwxqtnLl+8/gdVNepxBuHI4erxU0sbKTkD9Yw2RDfc7ldz/JW04WPljImZZdFihhGoZ0iHl+tQVM9w2y+84oymp353B85qASNnTMws9+eH5YoZKd22a4/qGYXiyMJXv6QwzJa5J2dXImlbILz89YyjZz+1pyz+ 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alt="" height="318" width="532"><br>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.<br>Did any one get a better result? Does any one have some suggestion about the parameters or others?<br><br>Thank you.<br><br>An Qi<br>Tokyo University of Agriculture and Technology - Nakagawa Laboratory<br>2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588<br>[email protected]<br></body></html>
<html><body>Hello.<br>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. <br>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. <br> <img
src="data:image/png;base64,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
- mnist test result on SP+KNN 50014646142
- mnist test result on SP+KNN 50014646142
- Re: mnist test result on SP+KNN Fergal Byrne
- Re: mnist test result on SP+KNN Eric Laukien
- Re: mnist test result on SP+KNN 50014646142
- Re: mnist test result on SP+KNN Eric Laukien
- Re: mnist test result on SP+KNN Chandan Maruthi
- Re: mnist test result on SP+KNN Subutai Ahmad
- Re: mnist test result on SP+KNN 50014646142
- Re: mnist test result on SP+KNN Subutai Ahmad
- Re: mnist test result on SP+KNN 50014646142
