V2-refactor - Higher train errors with eval=val

[dmlc]

V2-refactor - Higher train errors with eval=val

[DataCentric1]

Thinking more about it, it makes sense train error could also be affected if we are modifying the params (LR, bias etc) adaptively based on the val error (which is really great!).

Still, the train error on first iteration is much higher with "eval = val" (0.76) vs. "eval = train" (0.61)?

[antinucleon]

Do you use CUDNN? I find there is some unstable stuff in CUDNN pooling, which makes unpredictable result. Now I disabled CuDNN pooling.

[DataCentric1]

No, I don't use CUDNN. I'm planning to run the exact same model in both master and v2-refac and see if my submission scores in the competition are any different. Will update with what I find.

[DataCentric1]

Ok, something's definitely amiss. I ran the exact same bowl.conf and pred.conf files (just minor chnages to be rev compatible) in both master and v2-refactor. My train error / val error in Master was .227 / .257 and in V2-refac was .342 / .296.

When I submitted in Kaggle, leaderboard score for master was 0.90 and V2-refac was 0.98. This was only one single submission with no averaging of multiple outputs. Something definitely seems off with using v2-refac or I'm missing some details?

FYI, this is the older master from ~6 weeks back, I haven't updated at all.

[antinucleon]

Thanks very much! I will check my configuration tomorrow. If possible, could you share me your configuration? just email me: antinucleon àt gmail.com so that I will be more clear of what happened. I used V2 for all competition, and I didn't find out any abnormal.

[antinucleon]

I re-run the experiment again. result is:
new:
[312] train-error:0.237291 train-logloss:0.712367 val-error:0.232272 val-logloss:0.725718
old:
[312] train-error:0.244916 train-logloss:0.745804 val-error:0.241366 val-logloss:0.7564

So I don't think it is CXXNET's problem.