wehub-resource-sync
89 lines
1.4 KiB
Bash
89 lines
1.4 KiB
Bash
#!/bin/bash
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# input arguments
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DATA="${1-DD}" # ENZYMES, DD, PROTEINS, COLLAB, IMDB-BINARY, IMDB-MULTI
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device=${2-0}
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num_trials=${3-10}
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print_every=${4-10}
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# general settings
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hidden_gxn=96
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k1=0.8
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k2=0.7
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sortpooling_k=30
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hidden_final=128
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batch_size=64
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dropout=0.5
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cross_weight=1.0
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fuse_weight=0.9
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weight_decay=1e-3
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# dataset-specific settings
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case ${DATA} in
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IMDB-BINARY)
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num_epochs=200
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learning_rate=0.001
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sortpooling_k=31
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k1=0.8
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k2=0.5
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;;
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IMDB-MULTI)
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num_epochs=200
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learning_rate=0.001
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sortpooling_k=22
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k1=0.8
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k2=0.7
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;;
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COLLAB)
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num_epochs=100
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learning_rate=0.001
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sortpooling_k=130
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k1=0.9
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k2=0.5
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;;
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DD)
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num_epochs=100
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learning_rate=0.0005
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sortpooling_k=291
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k1=0.8
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k2=0.6
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;;
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PROTEINS)
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num_epochs=100
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learning_rate=0.001
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sortpooling_k=32
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k1=0.8
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k2=0.7
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;;
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ENZYMES)
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num_epochs=500
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learning_rate=0.0001
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sortpooling_k=42
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k1=0.7
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k2=0.5
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;;
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*)
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num_epochs=500
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learning_rate=0.00001
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;;
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esac
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python main.py \
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--dataset $DATA \
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--lr $learning_rate \
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--epochs $num_epochs \
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--hidden_dim $hidden_gxn \
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--final_dense_hidden_dim $hidden_final \
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--readout_nodes $sortpooling_k \
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--pool_ratios $k1 $k2 \
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--batch_size $batch_size \
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--device $device \
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--dropout $dropout \
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--cross_weight $cross_weight\
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--fuse_weight $fuse_weight\
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--weight_decay $weight_decay\
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--num_trials $num_trials\
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--print_every $print_every\
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