The first two copyfile commands are great, they pull the necessary functions into your current working directories. From there if we execute and follow the debug information we can discover the following.
The 'nn7' argument can be fed to train() or to net() but it appears it is ONLY essential for evaluation using net(). This is because when we set net.performFcn = 'mymse' the nntoolbox will automatically pick up our performance function and will use it for training (you can see it listed in the nntraintool). HOWEVER, if you then try to call net() without using the 'nn7' argument it will output all zeros. For whatever reason training detects the correct net.performFcn and uses it, but the evaluation function does not. You can see this from the command line or from the nntraintool which both show incorrect outputs of zero if net is just called blindly without the 'nn7' argument (as it is if the push buttons in the nntraintool GUI are used without alteration).
Another interesting aspect is that examining the templates for the cost functions it appears as if nn7 (or 'nnet7') is only currently implemented with mse, mae, msesparse, sae and sse cost functions, assuming that is what the 'nnet7' is doing. I don't know this for sure at this time.
If we stop the code when running inside the 'mymse' function, we see that initially the INFO data is setup and made persistent, and this carries details about which functions to use in evaluating various calls to performance. Presumably, if we want to modify the cost function (or override it) then this involves adjusting all these functions suitably. So we need to examine how we need to modify the following subfunctions,
@mymse.apply @mymse.backprop @mymse.forwardprop @mymse.dperf_dwb
Each of these functions can be found beneath the '+mymse' directory we just copied to our working directory. There appear to be other related functions there too (such as perfwb.m and perfw_to_ew.m and it may be necessary to modify them as well for at least consistency sake).
