I have processed some OCR figures (I will post a paper on this soon on my blog), the images are now contained in a <127 uint 8 array, so they can be trained into the currie 101 NN hardware NN.
My question is this
I train my NN with a set of data, it learns 10 images, and has 10 committed neurons
I then test my NN with the same data, and it recognises 19 out of 20 images correctly, one it gets wrong.
Is this normal/ok ?
Can I reduce this error by repeatedly training the same images say 10 times, so learn with the same data 10 times etc
What is better KNN or RBF ?
If it gets one wrong by classifing it incorrectly should I use category 0 to unlearn it, it say it gets image 16 wrong, the image is of a cat 7, when I ask the NN to classify the same image it comes back with a cat 8.
Should I get this image that it classifies wrong, and learn it into cat 0 ? Will this mean that it no longer will give me an incorrect answer, is there a general strategy for this situation ?
Thank you for contacting us.
About your first question, well it is normal and yes I think you should keep training it to reduce the error.
Regarding your doubts with KKN or RBF and classifying, I recommend you to check this link: https://www.general-vision.com/documentation/TM_NeuroMem_API.pdf, you should found a lot of helpful information there.
Have a nice weekend.