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update: I run a toy experiment, output and state are the same here (I guess all cases they are the same except when the input has zero values. Usually I am used to feed final state as for example in seq2seq model except when we are outputting a label for each word, then we use output)
Hi, quick question, don't we need to feed 'state' in decoder_body instead of 'output'? here in this line
dynamic-coattention-network-plus/question_answering/networks/dcn_plus.py
Line 402 in 5182d91
update: I run a toy experiment, output and state are the same here (I guess all cases they are the same except when the input has zero values. Usually I am used to feed final state as for example in seq2seq model except when we are outputting a label for each word, then we use output)
outputs:
[2 6]
[[-0.00059593 0.00258833 -0.21033162 -0.00067049 -0.14312631 -0.31653395]
[ 0.29717636 0.10479536 -0.04902191 0.02340557 0.0264852 0.41031399]]
states:
LSTMStateTuple(c=array([[ -8.47961660e-03, 4.81678136e-02, -7.84035921e-01,
-6.87910477e-04, -5.49614251e-01, -9.96938109e-01],
[ 4.05349284e-01, 1.13499925e-01, -4.16956961e-01,
4.35479954e-02, 6.23860434e-02, 4.93930310e-01]], dtype=float32), h=array([[-0.00059593, 0.00258833, -0.21033162, -0.00067049, -0.14312631,
-0.31653395],
[ 0.29717636, 0.10479536, -0.04902191, 0.02340557, 0.0264852 ,
0.41031399]], dtype=float32))
Note that 'h' and outputs are the same above. Also, note that in decoder_body shape of 'state' is N*H not [N, D, C]
dynamic-coattention-network-plus/question_answering/networks/dcn_plus.py
Line 449 in 5182d91
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