Hello, Subutai:
Thanks for your reply. It is really helpful. Actually, I tried to use
it before. But it seemed there was something wrong with the code.
after I fixed the error, and ran it. I found the classifier just
learned 1 pattern. Now it is working. Great.
The only thing not that good is that, it is really slow because the
program read the images from 60000+10000 files. i think it will be
more efficient to read images from the datasets directly. Currently i
find the SDR i got is not very distributed, because i set a high
localAreaDensity value in order to get a high accuracy of recognition.
Even so, the best result i got is about 89%. :(
I have some questions about the SDR and TP. Currently, i am trying to
test SP+KNN is because i think the SDR is really important for TP. If
the SDR from the SP is not good enough (KNN classifier didn't
recognize it as a right one), it would lead TP to another way when
train with frame sequences(same pattern). So, how good should the SDR
be, for TP? And if we got a bad SDR (KNN recognize it as another
pattern), how much will it effect TP and CLA classifier? The last
question is about HTM model. Recently, some deep learning models is on
fire among academies. Like CNN (Convolutional neural network ),
RNN-LSTM (recurrent neural network - long short term memory). If we
compare HTM with them, what kind of advantage and disadvantage does
HTM have?
Thank you.
An Qi
On Mon, 19 Jan 2015 17:54:16 -0800
Subutai Ahmad <[email protected]> wrote
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]
