Thanks for the reply, Pedro and Mark.
@Pedro
You're right, I'm not using SP or TP. I did tried to simply activate SP in
model_params.py, but it soon ran out of memory (I'm using 8 GB), so I have to
use few columns and the result does not get improved.
@Mark
I agree with point 3.
About point 1: I think you're right about the classifier and I would like to
know more details about how it is implemented, so if someone knows, please let
me know.
About point 2: the images are encoded in 1D arrays with 784 (28x28) features,
so I do lost topological information. But it is still a high dimensional space
in which the data shows good clustering, so that even the hyperplanes obtained
by simple logistic regression are capable of classifying the digits with good
accuracy (> 90%). That's why I would like to get more information about the
classifier itself.
Best regards,
Allan
Em Quarta-feira, 22 de Janeiro de 2014 15:28, Pedro Tabacof <[email protected]>
escreveu:
Marek's second point is of utmost importance for anyone doing image
classification. It would be awesome if someone could make 2D topology easily
available. Convolutional neural networks are so much better than regular neural
networks for image classification.
On Wed, Jan 22, 2014 at 3:18 PM, Marek Otahal <[email protected]> wrote:
Hi Allan,
>
>that was maybe me, it's great someone is working on the MNIST here!
>
>1/ I'm not 100% clear about the Classifier, but I think it's just a helper
>utility, unrelated to the HTM/CLA, so you've been testing performance of any
>algorithm the CLassifier implements (not CLA imho). So you'd want to create a
>CLA (with SP only) and place Classifier atop of it. The pipeline would look
>like: {MNIST-data[ith-example]} >>> CLA(without TP) >>>(you get SDR) >>>
>Classifier (add MNIST-label[ith-example]
>
>2/ I assume the mnist dataset is created from 2D images of hadwritten digits
>-> and just simply put in 1D array (??)
>Then you'll lose lot of topological info passing it to the CLA just as is. I
>think this will require ressurection of the Image Encoders that take into
>account distance for neighborhood pixels (each pixel has 8 neighboring px),
>this is used in inhibition etc.
>
>3/ You're probably overfitting, rather experiment with 80%/20% data split.
>
>Cheers, Mark
>
>
>
>
>On Wed, Jan 22, 2014 at 5:57 PM, Allan Inocêncio de Souza Costa
><[email protected]> wrote:
>
>
>>
>>Hi,
>>
>>
>>I read a question that someone else asked here, but I couldn't find the
question nor the answers (if any), so I will ask again, as I'm now working
around with the classifier.
>>
>>
>>I tried to apply the classifier to the task of handwritten recognition
using the MNIST dataset. The best result I got was an overall accuracy
of about 42% (by that I mean that after training the entire dataset, the
proportion of right predictions from the first to the last training
example was 42%), after playing a little with the encoders. Of course
this is better than the expected 10% accuracy of a random picker
algorithm, but it falls short of what is accomplished by other (linear)
algorithms. For those interested, I
attached a plot of the accuracy.
>>
>>
>>
>>So here comes the question: what are the inner workings of the classifier?
>>I'm puzzled as it doesn't have a SP. Can someone help or point to some
reading?
>>
>>
>>Best regards,
>>Allan
>>
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>>
>>
>
>
>--
>Marek Otahal :o)
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>
>
--
Pedro Tabacof,
Unicamp - Eng. de Computação 08.
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