Hi,
I know this has been asked a number of times in a number of ways, but I’m struggling in trying to get the SDR classifier to actually work as intended.
Has anyone managed to get the SDR classifier to work (at all) using a spatial pooler output (SDR) for an image ie MNIST.
And if so do you happen to know what is the best way to work with the classifier, i.e:
Did you need to run monotonically the recordNum variable or can you just set it to 0. I’m not sure why you would need to track each record in the case of MNIST classification.
Can patternNZ be fed the SP active columns along with the supplied label for the classification={“bucketIdx”: key, “actValue”: key} variable. It would seem to me that this is the correct case.
If i pass the SP SDR and train using this, and then also try and infer on the same SP SDR it provides 100% accuracy (which seems odd).
But providing it a set of test sample SDRs gives rise to 90% error, simply because the classifier always outputs the probabilities as [0, 0, 0, 0, 0, 0, 0, 0, 1], which predicts for ‘9’ and in the test set 10 out of 100 are 9.
Once again this seems odd as I would assume the classifier would have some variability in prediction.
It just doesn’t seem to work, so any help is appreciated.