Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Number of weights #38

Open
yp000925 opened this issue Apr 23, 2021 · 0 comments
Open

Number of weights #38

yp000925 opened this issue Apr 23, 2021 · 0 comments

Comments

@yp000925
Copy link

Hi,
Thanks for your work!

However, it looks a lit bit confused for me about the number of params/trainable weights.

From the paper, it looks like that each children capsule has its own weight matrix to get the "predict vector" for the parent capsule in next layer. For example, from "primarycaps (ConvCapsuleLayer) " to "conv_cap_2_1 (ConvCapsuleLayer)", there are 2 capsules in the Primarycaps, should the # of params be multiplied by 2? Say 25664*2 ?
The same question also raises in the following layers for me.

Any one could please help me figure out this problem? Thanks !

The following is a part of model params summary for your reference.
image

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant