Why b_IJ is shared between single batch examples. about capsnet-tensorflow

[AlexHex7]

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.

b_IJ = tf.constant(np.zeros([1, input.shape[1].value, self.num_outputs, 1, 1], dtype=np.float32))

[naturomics]

@AlexHex7 @pkubik AlexHex7 is right, b_IJ will be re-init to 0 at each batch, it's not shared between batches. Someone(not me) had done experiments about this problem and he told me it does work in that way.

[pkubik]

Oh, sorry @AlexHex7 @naturomics . I didn't formulate it correctly. I meant that it (b_IJ) is shared between all examples in a single batch. So I'm not sure whether it is correct to share b_IJ between different examples in the same batch.

[pkubik]

To be more specific what I suggest is to change the initialization of b_IJ to:
b_IJ = tf.constant(np.zeros([cfg.batch_size, input.shape[1].value, self.num_outputs, 1, 1], dtype=np.float32))
and remove the reduction from the last line of routing inner loop:
b_IJ += u_produce_v

[naturomics]

@pkubik I'm doing a experiment for this problem, please wait for the result of the experiment.

[naturomics]

@pkubik Now, I agree with you, though it makes the number of parameters b_IJ be batch_size-related, and experiment shows it doesn't make much difference. here is a related discussion for this problem, It might help us understand why

[Queequeg92]

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?

[naturomics]

@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 Fashion-MNIST that mentioned in issue #20 will see the difference, I will try it soon.