Results about transferLearning_PPI experiments about graphcl

[yyou1996]

Hi @hyp1231,

Below is my previous result log

0 0.6695203812316668 0.6639126131844449
1 0.6689575398079525 0.6578154819531342
2 0.688679302806011 0.6857136163297752
3 0.6836998338581497 0.6732908715258754
4 0.69713972798318 0.6849932255983389
5 0.6776616371025179 0.6742576016349859
6 0.678085934406664 0.6687597803949993
7 0.6714889003635183 0.6592118772987136
8 0.6725745492007931 0.6691230880465453
9 0.6807927650869883 0.669925179267206

CUDA version:

NVIDIA-SMI 450.51.05 Driver Version: 450.51.05 CUDA Version: 11.0

Also, I just put the environment file here.

Could it be environmental issue? Lets figure it out. Any update please let me know. Thanks!

[hyp1231]

@yyou1996 Thank you for the fast and comprehensive reply. I'll try to upgrade the driver of my devices and do experiments as soon as possible.

[yyou1996]

Please also try lr=1e-2 and lr=1e-4 here. Due to potential environmental issue, a little bit of tuning is supposed to be required.

[yyou1996]

That's great! Good luck with your following experiments!

[hyp1231]

Thanks for your advice!

When tuning lr=1e-4, results come to 67.34 ± 1.15, which is close to the results in the literature.

Details of result.log

0 0.6725159200324586 0.66603653940776
1 0.6657212107373389 0.6568356122380342
2 0.6687794432662423 0.6648277830180185
3 0.6985625261307801 0.68720311294303
4 0.6759798551079974 0.6692231385248839
5 0.6765493005622117 0.6736804953394853
6 0.7022692012839785 0.6951517668704265
7 0.6763894721533189 0.6615853768109135
8 0.6853784068489189 0.683059879327427
9 0.6850071688755396 0.6764286988851322

BTW, the experiments are carried out in NVIDIA-SMI 418.67 Driver Version: 418.67 CUDA Version: 10.1. Due to the difficulty of upgrading, I have not tested on CUDA 11.0 yet and still have no idea whether it's an environmental issue. If I find available devices for testing in the future, I'll update the results here.

All in all, thanks so much for the kindly reply and instruction. Just feel free to close this comment. :D

[ha-lins]

Hi @yyou1996,

I wonder how to install torch=1.0.1 with cuda=11.0 or cuda=10.1. I found that the conda way only supports cuda 9.0 and 10.0. Thanks!

# CUDA 9.0
conda install pytorch==1.0.1 torchvision==0.2.2 cudatoolkit=9.0 -c pytorch

# CUDA 10.0
conda install pytorch==1.0.1 torchvision==0.2.2 cudatoolkit=10.0 -c pytorch

[yyou1996]

Hi @ha-lins,

I try torch.version.cuda then it outputs 9.0.176. I will defer this problem to others since I am not expert in environment config...