( From Nick Woodall's github )
Comments on the following code in the ipython notebook to aid understanding
Code for our ICML 2022 paper Generative Flow Networks for Discrete Probabilistic Modeling by Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio.
Synthetic tasks
python -m synthetic.train --data checkerboard --lr 1e-3 --type tblb --hid_layer 3 --hid 256 --print_every 100 --glr 1e-3 --zlr 1 --rand_coef 0 --back_ratio 0.5 --lin_k 1 --warmup_k 1e5 --with_mh 1
Discrete image modeling
python -m deepebm.ebm --model mlp-256 --lr 1e-4 --type tblb --hid_layer 3 --hid 256 --glr 1e-3 --zlr 1 --rand_coef 0 --back_ratio 0.5 --lin_k 1 --warmup_k 5e4 --with_mh 1 --print_every 100 --mc_num 5