@ptrblck
All the inbuilt methods for the prune module are based on norm of parameters (weights and biases). Any suggestion if I want to prune connections based on activations (e.g. remove top 10% connections based on norm of activations)? Should I try implementing it by subclassing or do you know if it has been implemented someplace else?
Also is it easy to switch the ranking of torch.nn.utils.prune.l1_unstructured? It says “removing the specified amount of (currently unpruned) units with the lowest L1-norm”. What if I want to remove the ones with the highest L1-norm? Let’s assume I have an application for the opposite ranking. Not sure if it makes sense according to pruning literature,