export CUDA_VISIBLE_DEVICES=MIG-dfac4f62-f2db-55e4-8c7a-25d3a3f7f78a
python main.py --model linformer --data_dir Multivariate_ts/EthanolConcentration --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 &
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 2010 &
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 &
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 2010 &
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 &
export CUDA_VISIBLE_DEVICES=MIG-e7d7c833-7141-5e89-8c37-5f53cafd315a
python main.py --model linformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
python main.py --model performer_mra_head --data_dir Multivariate_ts/EthanolConcentration --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 &
python main.py --model performer_mra_head --data_dir Multivariate_ts/EthanolConcentration --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 &
python main.py --model performer --data_dir Multivariate_ts/EthanolConcentration --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 &
python main.py --model performer --data_dir Multivariate_ts/EthanolConcentration --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 &
python main.py --model performer --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 2010 &
export CUDA_VISIBLE_DEVICES=MIG-37820fec-ed84-55b7-83fc-e1d35afcbf6
python main.py --model performer --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 &
python main.py --model performer --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
python main.py --model performer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 &
python main.py --model performer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
python main.py --model fmm_transformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 &
python main.py --model fmm_transformer_mra_head --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
export CUDA_VISIBLE_DEVICES=MIG-47d82139-3a34-5a1f-80fe-36af994f32b7
python main.py --model fmm_transformer --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 2010 &
python main.py --model fmm_transformer --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --output_dir output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 &
wait
echo "Done"