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Why b_IJ is shared between single batch examples. #21
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It seems that b_IJ will be init to 0 each batch. Or each time when we invoke call function, the b_IJ will be create to 0. I'm not sure whether my opinion is right, I have not use tensorflow for a long time.
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Oh, sorry @AlexHex7 @naturomics . I didn't formulate it correctly. I meant that it ( |
To be more specific what I suggest is to change the initialization of |
@pkubik I'm doing a experiment for this problem, please wait for the result of the experiment. |
Different sample, different object, different entities, so different b_ij. I'm wondering how much difference it makes@naturomics? Did you make the experiment on minist? |
@Queequeg92 Yeah, I did some experiments on mnist. It doesn't seem to make much difference in terms of classification accuracy. So I didn't release the corresponding result. Maybe trying it on the |
Forgive me if I got this wrong but it seems like the
b_IJ
are shared between all examples within a single batch (see reduce_sum and the shape).I didn't see any mention of the batches in the paper, so I have assumed that there is a separate set of
b_IJ
weights for every batch. Why do you think that it's better to share those variables?Edit:
I've corrected the statement:
to:
which is was I originally meant.
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