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View Code? Open in Web Editor NEWA PyTorch implementation of the iterative pruning method described in Han et. al. (2015)
A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)
Hi, thanks for the code!
According to deep compression, the pruned weights should stay zero in the later runs. However,
due to the functionality of momentum, the values of the newly pruned weights will be changed by the optimizer, since the grads of these weights aren't zero in the last epoch.
Do you have any idea to solve this problem without setting the momentum to zero?
Hi, Thanks for this repo. Just wondering if there is a deep compression algorithm for yolov3 darknet that you are aware of OR can that be used using this repo? Please suggest.
You did a very nice implement
But I want to ask for the weight that got masked by zero in weights.
Did the whole computation increase but weight's value are zero?
or the computation speed is just normal?
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