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]
