Hi, I had a quick look into gradCam and it seems to be very complex. Do you have any good resources on it?

That would be fantastic thanks!

Thank you, I think this is a nice fallback idea if all else fails. I'd like to be able to use some more sophisticated methods to identify regions of parameter space that come close to producing a set of outputs but this could be a place to start.

And looking at their post history, no I don't think that is my partner. Just another student with a similar problem in need of people to bounce ideas off of. :)

>This would take a lot of time most likely and could be a false positive

Could you use a gradient approach to speed this up? I don't know how you would do this, but if you could find the gradient of the search space you could use this to try and minimize for a certain set of parameters.

What do you think?

I had thought of this but unfortunately, I don't think that I have enough information in my output variables to be able to predict the inputs. When reversing the network the error bounces around and my accuracy stays consistently at ~10%.

I'm sorry I don't understand, are you saying that I answered the question in that you cannot do it? That you can't predict a likely input parameter for a network using a set of known outputs? It seems like you very much can do this, but that my approach to solving the problem is wrong. Do you have any suggestions as to how this could be done?