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fig_models/vgg16/sam_001/_sam_0.01vgg16_1_100.model

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fig_models/vgg16/sam_001/_sam_0.01vgg16_1_train_loss.json

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fig_models/vgg16/sam_001/dirty_mnist_vgg16_sam_001_command.txt

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+CUDA_VISIBLE_DEVICES=3 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.01 \
+    > train1_.log 2>&1 &

fig_models/vgg16/sam_002/_sam_0.02vgg16_1_100.model

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fig_models/vgg16/sam_002/_sam_0.02vgg16_1_50.model

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fig_models/vgg16/sam_002/_sam_0.02vgg16_1_train_loss.json

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fig_models/vgg16/sam_002/dirty_mnist_vgg16_sam_002_command.txt

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+CUDA_VISIBLE_DEVICES=2 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.02 \
+    > train_.log 2>&1 &

fig_models/vgg16/sam_005/_sam_0.05vgg16_1_100.model

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fig_models/vgg16/sam_005/_sam_0.05vgg16_1_50.model

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fig_models/vgg16/sam_005/_sam_0.05vgg16_1_train_loss.json

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fig_models/vgg16/sam_005/dirty_mnist_vgg16_sam_005_command.txt

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+CUDA_VISIBLE_DEVICES=0 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.05 \
+    > train_.log 2>&1 &

fig_models/vgg16/sam_01/_sam_0.1vgg16_1_100.model

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fig_models/vgg16/sam_01/_sam_0.1vgg16_1_50.model

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fig_models/vgg16/sam_01/_sam_0.1vgg16_1_train_loss.json

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fig_models/vgg16/sam_01/dirty_mnist_vgg16_sam_01_command.txt

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+CUDA_VISIBLE_DEVICES=1 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.1 \
+    > train1_.log 2>&1 &

fig_models/vgg16/sam_02/_sam_0.2vgg16_1_100.model

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fig_models/vgg16/sam_02/_sam_0.2vgg16_1_50.model

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fig_models/vgg16/sam_02/_sam_0.2vgg16_1_train_loss.json

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fig_models/vgg16/sam_02/dirty_mnist_vgg16_sam_02_command.txt

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+CUDA_VISIBLE_DEVICES=2 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.2 \
+    > train2_.log 2>&1 &

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fig_models/vgg16/sam_05/_sam_0.5vgg16_1_train_loss.json

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fig_models/vgg16/sam_05/dirty_mnist_vgg16_sam_05_command.txt

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+CUDA_VISIBLE_DEVICES=3 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sam \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    --rho 0.5 \
+    > train3_.log 2>&1 &

fig_models/vgg16/sgd_000/_sgd_0vgg16_1_100.model

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fig_models/vgg16/sgd_000/_sgd_0vgg16_1_train_loss.json

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fig_models/vgg16/sgd_000/dirty_mnist_vgg16_sgd_000_command.txt

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+CUDA_VISIBLE_DEVICES=3 nohup python train.py \
+    --seed 1 \
+    --dataset dirty_mnist \
+    --dataset-root ./amnist/ \
+    --model vgg16 \
+    --opt sgd \
+    -e 100 \
+    --coeff 3.0 \
+    --save-interval 50 \
+    --save-path models/ \
+    > train1_.log 2>&1 &