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2020-02-03 04:54:39 Iteration 550 Training Loss: 1.706e-01 Loss in Target Net: 3.291e-02
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2020-02-03 04:55:25 Iteration 600 Training Loss: 1.707e-01 Loss in Target Net: 3.604e-02
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2020-02-03 04:56:06 Iteration 650 Training Loss: 1.688e-01 Loss in Target Net: 3.406e-02
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2020-02-03 04:56:37 Iteration 700 Training Loss: 1.763e-01 Loss in Target Net: 3.444e-02
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2020-02-03 04:57:19 Iteration 750 Training Loss: 1.699e-01 Loss in Target Net: 3.075e-02
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2020-02-03 04:57:56 Iteration 800 Training Loss: 1.692e-01 Loss in Target Net: 3.891e-02
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2020-02-03 04:58:27 Iteration 850 Training Loss: 1.708e-01 Loss in Target Net: 2.803e-02
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2020-02-03 04:59:08 Iteration 900 Training Loss: 1.683e-01 Loss in Target Net: 3.460e-02
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2020-02-03 04:59:52 Iteration 950 Training Loss: 1.677e-01 Loss in Target Net: 3.253e-02
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2020-02-03 05:00:29 Iteration 1000 Training Loss: 1.706e-01 Loss in Target Net: 3.369e-02
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2020-02-03 05:01:05 Iteration 1050 Training Loss: 1.655e-01 Loss in Target Net: 3.532e-02
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2020-02-03 05:01:47 Iteration 1100 Training Loss: 1.679e-01 Loss in Target Net: 3.794e-02
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2020-02-03 05:02:19 Iteration 1150 Training Loss: 1.709e-01 Loss in Target Net: 3.761e-02
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2020-02-03 05:02:56 Iteration 1200 Training Loss: 1.666e-01 Loss in Target Net: 3.143e-02
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2020-02-03 05:03:35 Iteration 1250 Training Loss: 1.685e-01 Loss in Target Net: 3.230e-02
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2020-02-03 05:04:14 Iteration 1300 Training Loss: 1.670e-01 Loss in Target Net: 2.831e-02
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2020-02-03 05:04:38 Iteration 1350 Training Loss: 1.696e-01 Loss in Target Net: 2.792e-02
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2020-02-03 05:05:18 Iteration 1400 Training Loss: 1.686e-01 Loss in Target Net: 4.217e-02
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2020-02-03 05:05:52 Iteration 1450 Training Loss: 1.704e-01 Loss in Target Net: 3.781e-02
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2020-02-03 05:06:31 Iteration 1499 Training Loss: 1.641e-01 Loss in Target Net: 3.673e-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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2020-02-03 05:06:45, Epoch 0, Iteration 7, loss 0.266 (0.436), acc 86.538 (90.600)
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2020-02-03 05:08:38, 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:[-2.1535134, -2.6043177, 0.37454954, 1.3219787, -2.3207252, -2.7911391, 4.7987614, -2.3489115, 7.6020384, -1.6695269], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 05:10:22 Epoch 59, Val iteration 0, acc 94.000 (94.000)
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2020-02-03 05:10:36 Epoch 59, Val iteration 19, acc 92.400 (92.740)
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* Prec: 92.74000129699706
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 19
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TARGET INDEX: 21
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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='2', 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=22, 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/1500/22
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:46:47 Iteration 0 Training Loss: 9.953e-01 Loss in Target Net: 1.326e+00
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2020-02-02 11:47:04 Iteration 50 Training Loss: 2.414e-01 Loss in Target Net: 6.717e-02
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2020-02-02 11:47:22 Iteration 100 Training Loss: 2.116e-01 Loss in Target Net: 4.076e-02
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2020-02-02 11:47:41 Iteration 150 Training Loss: 1.989e-01 Loss in Target Net: 4.196e-02
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2020-02-02 11:47:59 Iteration 200 Training Loss: 1.974e-01 Loss in Target Net: 3.746e-02
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2020-02-02 11:48:18 Iteration 250 Training Loss: 1.912e-01 Loss in Target Net: 3.297e-02
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2020-02-02 11:48:38 Iteration 300 Training Loss: 1.854e-01 Loss in Target Net: 3.032e-02
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2020-02-02 11:48:58 Iteration 350 Training Loss: 1.870e-01 Loss in Target Net: 3.141e-02
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2020-02-02 11:49:16 Iteration 400 Training Loss: 1.819e-01 Loss in Target Net: 3.218e-02
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2020-02-02 11:49:33 Iteration 450 Training Loss: 1.793e-01 Loss in Target Net: 3.492e-02
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2020-02-02 11:49:51 Iteration 500 Training Loss: 1.827e-01 Loss in Target Net: 3.049e-02
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2020-02-02 11:50:08 Iteration 550 Training Loss: 1.777e-01 Loss in Target Net: 3.520e-02
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2020-02-02 11:50:27 Iteration 600 Training Loss: 1.765e-01 Loss in Target Net: 2.920e-02
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2020-02-02 11:50:45 Iteration 650 Training Loss: 1.787e-01 Loss in Target Net: 3.015e-02
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2020-02-02 11:51:03 Iteration 700 Training Loss: 1.755e-01 Loss in Target Net: 3.929e-02
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2020-02-02 11:51:22 Iteration 750 Training Loss: 1.789e-01 Loss in Target Net: 3.042e-02
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2020-02-02 11:51:40 Iteration 800 Training Loss: 1.794e-01 Loss in Target Net: 3.487e-02
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2020-02-02 11:51:59 Iteration 850 Training Loss: 1.819e-01 Loss in Target Net: 3.252e-02
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2020-02-02 11:52:19 Iteration 900 Training Loss: 1.746e-01 Loss in Target Net: 2.667e-02
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2020-02-02 11:52:37 Iteration 950 Training Loss: 1.758e-01 Loss in Target Net: 2.864e-02
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2020-02-02 11:52:57 Iteration 1000 Training Loss: 1.728e-01 Loss in Target Net: 2.373e-02
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2020-02-02 11:53:16 Iteration 1050 Training Loss: 1.750e-01 Loss in Target Net: 3.174e-02
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2020-02-02 11:53:34 Iteration 1100 Training Loss: 1.725e-01 Loss in Target Net: 3.335e-02
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2020-02-02 11:53:51 Iteration 1150 Training Loss: 1.776e-01 Loss in Target Net: 2.749e-02
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2020-02-02 11:54:10 Iteration 1200 Training Loss: 1.748e-01 Loss in Target Net: 2.633e-02
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2020-02-02 11:54:31 Iteration 1250 Training Loss: 1.704e-01 Loss in Target Net: 2.620e-02
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2020-02-02 11:54:51 Iteration 1300 Training Loss: 1.719e-01 Loss in Target Net: 2.924e-02
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2020-02-02 11:55:10 Iteration 1350 Training Loss: 1.704e-01 Loss in Target Net: 2.584e-02
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2020-02-02 11:55:28 Iteration 1400 Training Loss: 1.722e-01 Loss in Target Net: 2.440e-02
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2020-02-02 11:55:49 Iteration 1450 Training Loss: 1.775e-01 Loss in Target Net: 2.381e-02
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2020-02-02 11:56:09 Iteration 1499 Training Loss: 1.720e-01 Loss in Target Net: 2.280e-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 11:56:19, Epoch 0, Iteration 7, loss 0.269 (0.440), acc 90.385 (91.600)
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2020-02-02 11:57:16, 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:6, Target's Score:[-3.8518815, 0.45647705, -2.6330886, -0.67095554, -0.5113298, -1.9255984, 7.347078, -4.0071425, 7.3386135, -1.2394398], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:58:16 Epoch 59, Val iteration 0, acc 91.800 (91.800)
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2020-02-02 11:58:24 Epoch 59, Val iteration 19, acc 92.200 (92.700)
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* Prec: 92.70000190734864
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 22
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DPN92 0
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='3', 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=23, 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/1500/23
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:44:39 Iteration 0 Training Loss: 9.976e-01 Loss in Target Net: 1.352e+00
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2020-02-02 11:44:58 Iteration 50 Training Loss: 2.272e-01 Loss in Target Net: 3.747e-02
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2020-02-02 11:45:14 Iteration 100 Training Loss: 1.961e-01 Loss in Target Net: 2.934e-02
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2020-02-02 11:45:30 Iteration 150 Training Loss: 1.815e-01 Loss in Target Net: 2.490e-02
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2020-02-02 11:45:47 Iteration 200 Training Loss: 1.756e-01 Loss in Target Net: 2.185e-02
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2020-02-02 11:46:06 Iteration 250 Training Loss: 1.683e-01 Loss in Target Net: 2.302e-02
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2020-02-02 11:46:23 Iteration 300 Training Loss: 1.658e-01 Loss in Target Net: 2.173e-02
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2020-02-02 11:46:39 Iteration 350 Training Loss: 1.674e-01 Loss in Target Net: 2.634e-02
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2020-02-02 11:46:57 Iteration 400 Training Loss: 1.655e-01 Loss in Target Net: 2.307e-02
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2020-02-02 11:47:15 Iteration 450 Training Loss: 1.640e-01 Loss in Target Net: 2.068e-02
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2020-02-02 11:47:31 Iteration 500 Training Loss: 1.620e-01 Loss in Target Net: 2.274e-02
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2020-02-02 11:47:47 Iteration 550 Training Loss: 1.651e-01 Loss in Target Net: 2.090e-02
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2020-02-02 11:48:06 Iteration 600 Training Loss: 1.608e-01 Loss in Target Net: 2.312e-02
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