Hi,
I am using Pytorch to try to build and train a multilayer perceptron which is aimed at mapping a 450 dimension input to a 120 dimension output. Actually the data I have is in complex matrix, so I vectorize it and concatenate its real&imaginary part together (and this is why my input and output are of this high dimension). I am now hoping to use a customized loss function which includes the matrix frobenius norm between the predicted results and the target.
I searched a lot on the forum but still not sure about how to implement this, since I am not sure whether pytorch will do the auto-gradient for it and whether I should also do something with the back-propagation of the customized loss function etc. At the same time, when dealing with this kind of complex matrix data, is there any better way to handle it? Or apart from MSE, is there any good loss fuction to deal with this kind of regression problem?
Thanks in advance!