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This repository has been archived by the owner on Nov 28, 2023. It is now read-only.
When you store loss values, do not forget to detach it from the computation graph (that is used for the back propagation) or you could run into memory issues ! :)
ex :
tot_loss += loss.item() # DON'T DO THAT
tot_loss += loss.detach().item() # DO THAT :D
DeepRank is using: running_loss += loss.data.item() in NeuralNet.py. Maybe we want to correct it.
The text was updated successfully, but these errors were encountered:
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From @manonreau on graphprot:
When you store loss values, do not forget to detach it from the computation graph (that is used for the back propagation) or you could run into memory issues ! :)
ex :
DeepRank is using:
running_loss += loss.data.item()
inNeuralNet.py
. Maybe we want to correct it.The text was updated successfully, but these errors were encountered: