export CUDA_VISIBLE_DEVICES=MIG-37820fec-ed84-55b7-83fc-e1d35afcbf63
python main.py --model linear_transformer --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 &
python main.py --model linear_transformer --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 &
python main.py --model linear_transformer --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 &
export CUDA_VISIBLE_DEVICES=MIG-e7d7c833-7141-5e89-8c37-5f53cafd315a
python main.py --model linear_transformer_mra_head --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 &
python main.py --model linear_transformer_mra_head --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 &
python main.py --model linear_transformer_mra_head --data_dir Multivariate_ts/FaceDetection --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 FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 &
wait
echo "Done"
