DeepHoldem
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happypepper
A question about training the neural networks
I tried to implement deepstack with python, and generated 4M training samples for the turn network. And I'm using exactly the same network structure as the author did.
But I found that with hundreds of epochs' training, the huber loss is about 0.2 on the training samples, which is far larger than the author's (0.016). Do you have any suggestions on training the network?
Thank you!
@happypepper Hi, thank you for your reply. I solve 2 streets instead of using a river network. And I calculated my exploitability of a turn case, the exploitability is around 2 mbb, so I guess my resolving process is right. Maybe there are bugs in my bucketing?
How many epochs did you use to train your network? I used thousands of epochs but my training loss is still very high.
After around 80 epochs, it stopped improving. Validation loss after first epoch was 0.08 already.
How did you do bucketing? k means + EMD?
@happypepper I use k-means on the river round, and EMD on other rounds. I used the same bucketing in the reference papers.
I noticed that in your code, you made a change when calculating the loss.
In line 64 in masked_huber_loss.lua, your code is:
local loss_multiplier = (batch_size * feature_size) / self.mask_sum:sum()
This means you average the loss on valid buckets, not on all the 1000 buckets. I think this makes sense, and the author's repo has a bug here.
Is there any way to debug my bucketing? I'm at the end of my rope.
how is it possible to use k-means for river? There is only one number instead of distribution. EMD is usually used in combination with k-means.
You can email me and we can communicate outside of github somehow, it's easier
@happypepper Hi, I just sent an email to you and described the method of generating river clusters.
