Gibbsnet issue

[LukasMosser]

I've been running the new gibbsnet implementation, but I can't find it to converge with the given settings.

Do you have any examples on what the results should look like?

[wiseodd]

My point on that implementation is to get the intuition of GibbsNet, and how to quickly implement it, esp. given ALI/BiGAN implementation. Given this intuition feel free to experiment with another architecture and hyperparams. Cheers, Agustinus

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-------- Original Message -------- Subject: [wiseodd/generative-models] Gibbsnet issue (#44) Local Time: December 25, 2017 12:32 PM UTC Time: December 25, 2017 11:32 AM From: ***@***.*** To: wiseodd/generative-models ***@***.***> Subscribed ***@***.***> I've been running the new gibbsnet implementation, but I can't find it to converge with the given settings. Do you have any examples on what the results should look like? — You are receiving this because you are subscribed to this thread. Reply to this email directly, [view it on GitHub](#44), or [mute the thread](https://github.com/notifications/unsubscribe-auth/ACMLkje0rgFcNA-3EBZphgQHDEY80WIsks5tD4e8gaJpZM4RMQnu).

[LukasMosser]

Yes, I understand.
I just wanted to point out that it seems like this doesn't converge though.
After 2000 iterations:

The loss of the generator is very high (18) and discriminator loss goes to zero.
Have you gotten it to work with your code? I am using the code as is, no modifications.

[wiseodd]

Yeah, it's also what I experienced. It won't generate good result and the loss is blowing up. I suspect we need to have stronger networks + much lower learning rate. However, as I'd like to have everything in this repo with very simple architecture, I decided to push it as is :)

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-------- Original Message -------- Subject: Re: [wiseodd/generative-models] Gibbsnet issue (#44) Local Time: December 25, 2017 5:27 PM UTC Time: December 25, 2017 4:27 PM From: ***@***.*** To: wiseodd/generative-models ***@***.***> Agustinus Kristiadi ***@***.***>, Comment ***@***.***> Yes, I understand. I just wanted to point out that it seems like this doesn't converge though. After 2000 iterations: [200](https://user-images.githubusercontent.com/4195648/34341482-56435620-e990-11e7-9ac8-c913aaad30b7.png) The loss of the generator is very high (18) and discriminator loss goes to zero. Have you gotten it to work with your code? I am using the code as is, no modifications. — You are receiving this because you commented. Reply to this email directly, [view it on GitHub](#44 (comment)), or [mute the thread](https://github.com/notifications/unsubscribe-auth/ACMLkkHr6i-Kq9UBCPRG9nVuxaijiUV2ks5tD8zjgaJpZM4RMQnu).

[LukasMosser]

I tried an 8 layer MLP for each network, as suggested in the paper with lr of 1e-5. Still no convergence.
Would be nice to get a version working. If I make any progress I'll update.