for beta in 0.55 0.65 0.75 0.85 0.95
do
    CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fourierformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 --weight_k $beta &

CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fourierformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 --weight_k $beta &

CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fourierformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 --weight_k $beta &
    wait 
    echo "Continue"
done






# #!/bin/bash 

# for se in 666 1608 2020; do
#     CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/InsectWingbeat --output_dir /tanData/tam/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 --model transformer --name InsectWingbeat --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 3 --num_heads 16 --d_model 128 --dim_feedforward 256 --seed $se
# done

# # # EthanolConcentration Handwriting Heartbeat JapaneseVowels SelfRegulationSCP2 PEMS-SF SelfRegulationSCP1 UWaveGestureLibrary SpokenArabicDigits FaceDetection

# # # EthanolConcentration
# # # CUDA_VISIBLE_DEVICES=1 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/EthanolConcentration --output_dir /tanData/tam/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 --model transformer --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 

# # # FaceDetection
# # CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/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 --model transformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 wait

# # #Handwriting
# # CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Handwriting --output_dir /tanData/tam/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 --model transformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 &

# # #Heartbeat
# # CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Heartbeat --output_dir /tanData/tam/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 --model transformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 &

# # #JapaneseVowels
# # CUDA_VISIBLE_DEVICES=1 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/JapaneseVowels --output_dir /tanData/tam/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 --model transformer --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 &

# # #PEMS-SF
# # CUDA_VISIBLE_DEVICES=1 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --output_dir /tanData/tam/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 --model transformer --name PEMS-SF --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 512 

# # #SelfRegulationSCP1
# # CUDA_VISIBLE_DEVICES=1 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP1 --output_dir /tanData/tam/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 --model transformer --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 

# # #SelfRegulationSCP2
# # CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --output_dir /tanData/tam/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 --model transformer --name SelfRegulationSCP2 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 

# # #SpokenArabicDigits
# # CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SpokenArabicDigits --output_dir /tanData/tam/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 --model transformer --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256  

# # #UWaveGestureLibrary
# # CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --output_dir /tanData/tam/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 --model transformer --name UWaveGestureLibrary --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 16 --d_model 256 --dim_feedforward 256 
 

# # # for task_ in EthanolConcentration Handwriting Heartbeat JapaneseVowels SelfRegulationSCP2 PEMS-SF SelfRegulationSCP1 UWaveGestureLibrary SpokenArabicDigits FaceDetection; do

# # python src/main.py --output_dir experiments --comment "classification from Scratch" --name $1_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/EthanolConcentration --output_dir /tanData/tam/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 --model transformer --name $task_ --seed 2010\
# # --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256\
# # --pos_encoding learnable --dropout 0.01 --activation gelu 



