Hi everyone,
I’m currently trying to reproduce the results from “Residual networks behave like ensembles of relatively shallow networks” (https://arxiv.org/abs/1605.06431 4) with pytorch, and was wondering if there is any way to select specific layers (e.g. inputs / outputs of the 40th residual block) within a network. The closest to an answer that I can think of is either using loss.creator.previous_functions[0][0] in a chained manner, or manually listing the hundreds of residual layers when building a model and assigning a return function to each of them. Any advice out of pytorch would also be greatly appreciated. Thanks