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

Share an example notebook #17

Open
aadimator opened this issue Mar 29, 2021 · 5 comments
Open

Share an example notebook #17

aadimator opened this issue Mar 29, 2021 · 5 comments

Comments

@aadimator
Copy link

First of all, thanks for such an awesome implementation. One suggestion is to add a simple example training notebook so one can easily understand how to use this on an actual dataset like IMDB and such. That'd would be huge help. Thanks

@batrlatom
Copy link

batrlatom commented Mar 31, 2021

What about this?

https://colab.research.google.com/drive/1rCZWPpFlgPZC_sqiUtKRSf16rScJi0JW?usp=sharing

I have prepared notebook for very bare 'object detection' . Would like to improve on it later on

@lucidrains Is this sufficient to show how perceiver works?

@lucidrains
Copy link
Owner

@batrlatom I think it would be better to run this on cifar-10 or 100!

@batrlatom
Copy link

batrlatom commented Mar 31, 2021

I will do an example on cifar100

@lzmax888
Copy link

lzmax888 commented Apr 1, 2021

@batrlatom How do you determine your hyperparameters? Such as num_freq_bands and max_freq

BTW,
"
attn_dropout = 0.5,
ff_dropout = 0.5,
"
half dropped out?

@batrlatom
Copy link

It was just trial and error. It worked for me on mnist and my custom problem with object detection. Honestly, not so much on cifar. I am trying the original params now.

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

4 participants