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run_mnist_training.sh
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python train.py --dataset mnist --norm Linf --hinge_maxeps 0.45 --seed 0 --savepath ./trained_models/mnist-Linf-MMA-0.45-sd0
python train.py --dataset mnist --norm Linf --hinge_maxeps 0.45 --seed 0 --clean_loss_coeff 0. --savepath ./trained_models/mnist-Linf-OMMA-0.45-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 2. --seed 0 --savepath ./trained_models/mnist-L2-MMA-2.0-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 4. --seed 0 --savepath ./trained_models/mnist-L2-MMA-4.0-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 6. --seed 0 --savepath ./trained_models/mnist-L2-MMA-6.0-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 2. --seed 0 --clean_loss_coeff 0. --savepath ./trained_models/mnist-L2-OMMA-2.0-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 4. --seed 0 --clean_loss_coeff 0. --savepath ./trained_models/mnist-L2-OMMA-4.0-sd0
python train.py --dataset mnist --norm L2 --hinge_maxeps 6. --seed 0 --clean_loss_coeff 0. --savepath ./trained_models/mnist-L2-OMMA-6.0-sd0