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Hi @ryh95
I don't remember if there was any particular reason for moving the output to CPU. As I have mentioned in some other comments, this model was my first time using PyTorch, so I was quite extra cautious in many places, and ended up being redundant. This move to CPU might be one such instance.
Regarding the difference between state and hidden, I believe I had run experiments using both, and ended up using the one that performed better. Let me see if I can dig up the results.
I noticed that in test function of trainer module
you write
output = output.data.squeeze().cpu()
Why you move
output
to cpu ?By the way
in SimilarityTreeLSTM of model module
output = self.similarity(lstate, rstate)
Why don't use
output = self.similarity(lhidden, rhidden)
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