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View Code? Open in Web Editor NEWA basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.
Home Page: https://kaifishr.github.io/2021/12/15/relevance-propagation-pytorch.html
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.
Home Page: https://kaifishr.github.io/2021/12/15/relevance-propagation-pytorch.html
Dear @KaiFabi ,
Thanks for this implementation. I think it is super cool! Can your implementation be used for the regression problem where there is only one output neuron representing a numerical value. How should I change the implementation to do this, if possible?
Looking forward to hearing your comment on this!
Thanks
hi @kaifishr
Thanks for your implementation. I'm trying to reimplement lrp on Resnet50, but it has a BatchNorm2D layer in the backbone, I'm a freshman in python and I don't know how to code the RelevancePropagationBatchNorm2D
in lrp_layers.py. Can you just give me some ideas? Thanks a lot.
Hi @KaiFabi
Look like the lrp layer is worked if the network structure's model is sequential right?. I tried using squeezenet that has FIRE module (contain concatenate layer) got an error channels.
Thanks
Could you please explain the formula for Gradient-based Relevance Computation in more detail, I notice you have explained in your blog.
Part code:
z = self.layer.forward(a) + self.eps
s = (r / z).data
(z * s).sum().backward()
c = a.grad
r = (a * c).data
There are some questions:
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