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run_multi.sh
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wei_arr=(
0.0783
0.0620
0.0493
0.0394
0.0315
0.0252
0.0201
0.0161
0.0129
0.0103)
adj_arr=(
0.5656
0.5372
0.5102
0.4843
0.4598
0.4368
0.4149
0.3941
0.3737
0.3547)
for model in gcn
do
for data in cora citeseer pubmed Cornell Texas Wisconsin Computers Photo
do
for i in ${!wei_arr[@]}
do
for pr in 0.1 0.2 0.3
do
for uf in 10 20 30
do
for fpe in 50 100 150
do
python main_stgnn.py --method GraNet \
--prune-rate $pr \
--optimizer adam \
--sparse-init ERK \
--init-density 1.0 \
--final-density ${wei_arr[$i]} \
--final-density_adj ${adj_arr[$i]} \
--final-density_feature 1.0 \
--update-frequency $uf \
--l2 0.0005 \
--lr 0.01 \
--epochs 200 \
--model $model \
--data $data \
--final-prune-epoch $fpe \
--growth_schedule momentum \
--adj_sparse \
--weight_sparse \
--sparse
done
done
done
done
done
done
# --model: gcn, gat, sgc, appnp, gcnii (5)
# --data: cora, citeseer, citeseer, Cornell, Texas, Wisconsin, Actor
# --data: CS, Physics, Computers, Photo, WikiCS, reddit
# --data: ogbn-arxiv, ogbn-proteins, ogbn-products, ogbn-papers100M (17)
# --weight_sparse or --feature_sparse --sparse (7)
# --sparse: base or sparse train (2)
# --method: GraNet, GraNet_uniform, GMP, GMP_uniform (4)
# --growth_schedule: gradient, momentum, random (3)
# --sparse_init: uniform, ERK (2)
# --prune-rate : regenration rate : 0.1, 0.2, 0.3 (3)
# --update-frequency 10 20 30 (3)
# --final-prune-epoch 50 100 150 (3)
# --init-density: weight init density: 1, (dense to sparse)
# --final-density: weight : 0.5 0.1 0.01 0.0001 (4)
# --final-density_adj : 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.01 (10)
# --final-density_feature: 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.01 (10)
# 5 x 17 x 7 x 4 x 3 x 2 X 3 X 3 X 3 X 4 X 10 X 10 = 154,224,000
# Actual: 4 x 13 x 4 x 10 x 3 x 3 x 3 = 56160
# python main_stgnn.py --method GraNet \
# --prune-rate 0.5 \
# --optimizer adam \
# --sparse-init ERK \
# --init-density 0.5 \
# --final-density 0.1 \
# --update-frequency 10 \
# --l2 0.0005 \
# --lr 0.01 \
# --epochs 200 \
# --model gcn \
# --data cora \
# --final-prune-epoch 100