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2020-02-02 11:10:23, Epoch 0, Iteration 7, loss 0.855 (0.508), acc 82.692 (88.800)
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2020-02-02 11:11:22, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-2.6113892, 1.0819747, -4.220945, -1.3872055, -0.92797554, -3.426873, 5.813769, -2.648436, 10.7439165, -2.2859273], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:12:22 Epoch 59, Val iteration 0, acc 94.200 (94.200)
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2020-02-02 11:12:30 Epoch 59, Val iteration 19, acc 93.800 (93.280)
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* Prec: 93.28000183105469
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 4
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DPN92 1
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='0', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=40, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/40
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:43:58 Iteration 0 Training Loss: 9.941e-01 Loss in Target Net: 1.377e+00
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2020-02-02 12:44:16 Iteration 50 Training Loss: 2.577e-01 Loss in Target Net: 8.830e-02
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2020-02-02 12:44:33 Iteration 100 Training Loss: 2.353e-01 Loss in Target Net: 8.759e-02
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2020-02-02 12:44:51 Iteration 150 Training Loss: 2.138e-01 Loss in Target Net: 7.793e-02
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2020-02-02 12:45:08 Iteration 200 Training Loss: 2.094e-01 Loss in Target Net: 1.069e-01
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2020-02-02 12:45:25 Iteration 250 Training Loss: 2.065e-01 Loss in Target Net: 8.009e-02
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2020-02-02 12:45:41 Iteration 300 Training Loss: 2.082e-01 Loss in Target Net: 7.501e-02
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2020-02-02 12:45:58 Iteration 350 Training Loss: 1.972e-01 Loss in Target Net: 7.665e-02
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2020-02-02 12:46:17 Iteration 400 Training Loss: 1.983e-01 Loss in Target Net: 6.369e-02
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2020-02-02 12:46:34 Iteration 450 Training Loss: 2.024e-01 Loss in Target Net: 5.249e-02
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2020-02-02 12:46:51 Iteration 500 Training Loss: 1.950e-01 Loss in Target Net: 5.534e-02
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2020-02-02 12:47:09 Iteration 550 Training Loss: 1.920e-01 Loss in Target Net: 5.131e-02
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2020-02-02 12:47:26 Iteration 600 Training Loss: 2.031e-01 Loss in Target Net: 4.867e-02
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2020-02-02 12:47:44 Iteration 650 Training Loss: 1.892e-01 Loss in Target Net: 5.809e-02
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2020-02-02 12:48:01 Iteration 700 Training Loss: 1.933e-01 Loss in Target Net: 4.409e-02
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2020-02-02 12:48:19 Iteration 750 Training Loss: 1.915e-01 Loss in Target Net: 5.529e-02
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2020-02-02 12:48:38 Iteration 800 Training Loss: 1.859e-01 Loss in Target Net: 4.318e-02
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2020-02-02 12:48:56 Iteration 850 Training Loss: 1.864e-01 Loss in Target Net: 4.282e-02
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2020-02-02 12:49:13 Iteration 900 Training Loss: 1.896e-01 Loss in Target Net: 4.553e-02
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2020-02-02 12:49:30 Iteration 950 Training Loss: 1.934e-01 Loss in Target Net: 5.830e-02
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2020-02-02 12:49:47 Iteration 1000 Training Loss: 1.865e-01 Loss in Target Net: 5.369e-02
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2020-02-02 12:50:04 Iteration 1050 Training Loss: 1.833e-01 Loss in Target Net: 4.993e-02
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2020-02-02 12:50:21 Iteration 1100 Training Loss: 1.853e-01 Loss in Target Net: 5.851e-02
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2020-02-02 12:50:39 Iteration 1150 Training Loss: 1.855e-01 Loss in Target Net: 5.806e-02
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2020-02-02 12:50:58 Iteration 1200 Training Loss: 1.835e-01 Loss in Target Net: 4.626e-02
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2020-02-02 12:51:15 Iteration 1250 Training Loss: 1.873e-01 Loss in Target Net: 4.585e-02
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2020-02-02 12:51:32 Iteration 1300 Training Loss: 1.824e-01 Loss in Target Net: 4.619e-02
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2020-02-02 12:51:51 Iteration 1350 Training Loss: 1.885e-01 Loss in Target Net: 3.565e-02
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2020-02-02 12:52:07 Iteration 1400 Training Loss: 1.892e-01 Loss in Target Net: 4.309e-02
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2020-02-02 12:52:25 Iteration 1450 Training Loss: 1.861e-01 Loss in Target Net: 5.048e-02
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2020-02-02 12:52:42 Iteration 1499 Training Loss: 1.862e-01 Loss in Target Net: 4.519e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:52:51, Epoch 0, Iteration 7, loss 0.284 (0.496), acc 86.538 (89.200)
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2020-02-02 12:53:49, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-1.3191082, -1.1050663, 0.17192008, -2.1951604, -2.1467378, -3.5698721, 3.210537, -2.0955296, 10.893505, -1.4458033], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:54:49 Epoch 59, Val iteration 0, acc 92.800 (92.800)
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2020-02-02 12:54:57 Epoch 59, Val iteration 19, acc 92.200 (93.160)
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* Prec: 93.16000175476074
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 40
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DPN92 1
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=41, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/41
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:45:12 Iteration 0 Training Loss: 1.029e+00 Loss in Target Net: 1.422e+00
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2020-02-02 12:45:28 Iteration 50 Training Loss: 2.699e-01 Loss in Target Net: 1.236e-01
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2020-02-02 12:45:45 Iteration 100 Training Loss: 2.416e-01 Loss in Target Net: 9.964e-02
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2020-02-02 12:46:01 Iteration 150 Training Loss: 2.315e-01 Loss in Target Net: 8.202e-02
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2020-02-02 12:46:17 Iteration 200 Training Loss: 2.239e-01 Loss in Target Net: 7.905e-02
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2020-02-02 12:46:35 Iteration 250 Training Loss: 2.156e-01 Loss in Target Net: 7.581e-02
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2020-02-02 12:46:53 Iteration 300 Training Loss: 2.102e-01 Loss in Target Net: 6.558e-02
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2020-02-02 12:47:12 Iteration 350 Training Loss: 2.104e-01 Loss in Target Net: 5.838e-02
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2020-02-02 12:47:28 Iteration 400 Training Loss: 2.145e-01 Loss in Target Net: 5.475e-02
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2020-02-02 12:47:47 Iteration 450 Training Loss: 2.157e-01 Loss in Target Net: 5.632e-02
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2020-02-02 12:48:05 Iteration 500 Training Loss: 2.030e-01 Loss in Target Net: 5.253e-02
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2020-02-02 12:48:21 Iteration 550 Training Loss: 2.030e-01 Loss in Target Net: 5.611e-02
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2020-02-02 12:48:39 Iteration 600 Training Loss: 2.032e-01 Loss in Target Net: 5.738e-02
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2020-02-02 12:48:57 Iteration 650 Training Loss: 2.062e-01 Loss in Target Net: 5.453e-02
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2020-02-02 12:49:15 Iteration 700 Training Loss: 2.113e-01 Loss in Target Net: 5.042e-02
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2020-02-02 12:49:31 Iteration 750 Training Loss: 2.019e-01 Loss in Target Net: 4.913e-02
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2020-02-02 12:49:48 Iteration 800 Training Loss: 2.042e-01 Loss in Target Net: 4.799e-02
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2020-02-02 12:50:06 Iteration 850 Training Loss: 2.019e-01 Loss in Target Net: 5.115e-02
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2020-02-02 12:50:23 Iteration 900 Training Loss: 1.986e-01 Loss in Target Net: 5.259e-02
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2020-02-02 12:50:40 Iteration 950 Training Loss: 1.971e-01 Loss in Target Net: 4.747e-02
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2020-02-02 12:50:57 Iteration 1000 Training Loss: 1.972e-01 Loss in Target Net: 5.923e-02
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2020-02-02 12:51:17 Iteration 1050 Training Loss: 2.019e-01 Loss in Target Net: 5.093e-02
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2020-02-02 12:51:34 Iteration 1100 Training Loss: 1.968e-01 Loss in Target Net: 5.139e-02
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2020-02-02 12:51:51 Iteration 1150 Training Loss: 1.989e-01 Loss in Target Net: 4.550e-02
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2020-02-02 12:52:10 Iteration 1200 Training Loss: 2.011e-01 Loss in Target Net: 5.283e-02
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2020-02-02 12:52:29 Iteration 1250 Training Loss: 1.997e-01 Loss in Target Net: 5.192e-02
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2020-02-02 12:52:47 Iteration 1300 Training Loss: 1.944e-01 Loss in Target Net: 4.652e-02
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2020-02-02 12:53:06 Iteration 1350 Training Loss: 2.029e-01 Loss in Target Net: 4.948e-02
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2020-02-02 12:53:22 Iteration 1400 Training Loss: 1.983e-01 Loss in Target Net: 5.276e-02
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2020-02-02 12:53:40 Iteration 1450 Training Loss: 1.976e-01 Loss in Target Net: 4.791e-02
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2020-02-02 12:53:57 Iteration 1499 Training Loss: 1.973e-01 Loss in Target Net: 4.764e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:54:07, Epoch 0, Iteration 7, loss 0.462 (0.366), acc 90.385 (90.800)
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2020-02-02 12:55:06, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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