Hi all,
I am in a bit of a predicament here. So right now, I am combining a neural network implemented in PyTorch and a conditional random field (graphical model) which is implemented in C++. I am passing in the output of the 2nd last layer of the neural network to the CRF. The CRF then uses that layer to calculate an AUC for its performance and also calculates the gradient of the second last layer. I then receive the gradient that the CRF calculated and place it in the .grad attribute of the second last layer.
At this point, my problem arises. I want to start back-propagation from this layer to all other parameters in my network, but this is hard to figure out to do without calling loss.backward() since each parameter is its own leaf node in the computational graph. Does anyone know any legit ways or any “hacky” ways to solve this?
Thanks!