**i have 3 sets of images on which i have trained neuroph. But when i try to recognize random images that is completely not matching my sample data set, it gets an accuracy nearing to 98%. How can this happen?
for example my training data set is a sample set of car images but when i take the picture of a computer mice it recognises it as one of the cars with 98% accuracy.
Can some one help me why it is happening.**
Use so called junk images, or in other words specify images that should not be recognized
Ususally these are all red, all green and all blue images in order to avoid unwanred recognition in case of high activation levels on all inputs.
Since neuroph is using rgb data as inputs, all white image woul dput high activation on all neurons and it would result as recognition, In order to avoid this we put these in junk images. You can specify these images using wizard in Neuroph Studio.
Also i would like to know what is the correct behaviour and what would be the result in percentages , if i add an image as junk and give that same image to neuroph for recognition.
will i get 0 as the result???
Thanks in Advance
Yes, usually something close to zero ( or far enough from 1)
Not sure ..but in most cases for me it doesn't play well . added to junk still get a match from the trained set and values are not so close enough to zero.
Any suggestions on how can i improve the results zoran.
Try different number of hidden neurons
Try different learning rate, momentum
If that doesent work try adding more layers of hidden neurons…