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

Weight Initialization #38

Open
jonny-d opened this issue Sep 12, 2017 · 4 comments
Open

Weight Initialization #38

jonny-d opened this issue Sep 12, 2017 · 4 comments

Comments

@jonny-d
Copy link

jonny-d commented Sep 12, 2017

Hello,

Thank you very much for sharing this code. I am attempting to re-train a model like this from scratch and was wondering which weight initialization method was used for training the model?

Thanks,
Jonny

@raulpuric
Copy link

Without any regularization I personally found that uniform sampling gave faster convergence, but was more unstable and blew up (see my issue #39). I also tried xavier initialization and that seemed to be more stable.

@jonny-d
Copy link
Author

jonny-d commented Sep 21, 2017

Hello, sorry for the delayed response.

I have achieved pretty good performance using a normal distribution for the initial weights. Here is a link to my Tensorflow Implementation

@raulpuric
Copy link

raulpuric commented Oct 9, 2017

@jonnykira I found like you that they used weight norm in the paper which I initially glossed over/isn't in the code base. This turned out to be what I needed to use.

@nkooli
Copy link

nkooli commented Oct 18, 2017

@jonnykira
hello,
I trained the model on my dataset, this generates me three files : model.data, model.index, model.meta

How can i generates the 15 .npy weight files (0.npy, 1.npy, ..., 14.npy) to test the sentiment analysis code (as in in https://github.com/openai/generating-reviews-discovering-sentiment) ?

thank you !

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

3 participants