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2020-02-02 12:03:46 Iteration 850 Training Loss: 2.006e-01 Loss in Target Net: 5.144e-02
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2020-02-02 12:04:05 Iteration 900 Training Loss: 1.997e-01 Loss in Target Net: 4.196e-02
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2020-02-02 12:04:25 Iteration 950 Training Loss: 1.934e-01 Loss in Target Net: 5.517e-02
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2020-02-02 12:04:43 Iteration 1000 Training Loss: 1.953e-01 Loss in Target Net: 5.885e-02
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2020-02-02 12:05:01 Iteration 1050 Training Loss: 1.967e-01 Loss in Target Net: 5.580e-02
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2020-02-02 12:05:18 Iteration 1100 Training Loss: 2.016e-01 Loss in Target Net: 4.692e-02
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2020-02-02 12:05:36 Iteration 1150 Training Loss: 1.952e-01 Loss in Target Net: 4.800e-02
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2020-02-02 12:05:55 Iteration 1200 Training Loss: 1.956e-01 Loss in Target Net: 4.781e-02
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2020-02-02 12:06:12 Iteration 1250 Training Loss: 1.959e-01 Loss in Target Net: 6.631e-02
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2020-02-02 12:06:30 Iteration 1300 Training Loss: 1.951e-01 Loss in Target Net: 5.737e-02
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2020-02-02 12:06:48 Iteration 1350 Training Loss: 1.929e-01 Loss in Target Net: 5.783e-02
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2020-02-02 12:07:05 Iteration 1400 Training Loss: 1.907e-01 Loss in Target Net: 5.875e-02
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2020-02-02 12:07:23 Iteration 1450 Training Loss: 1.944e-01 Loss in Target Net: 5.233e-02
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2020-02-02 12:07:40 Iteration 1499 Training Loss: 1.964e-01 Loss in Target Net: 4.905e-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 12:07:49, Epoch 0, Iteration 7, loss 0.469 (0.492), acc 86.538 (90.600)
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2020-02-02 12:08:47, 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:[-0.80621535, -0.9256004, -2.2733717, -2.4074485, -0.87339526, -1.888806, 5.204537, -1.2545661, 6.520925, -0.91729355], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:09:47 Epoch 59, Val iteration 0, acc 93.000 (93.000)
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2020-02-02 12:09:54 Epoch 59, Val iteration 19, acc 91.600 (92.960)
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* Prec: 92.96000137329102
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 26
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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='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=27, 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/1500/27
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:55:41 Iteration 0 Training Loss: 9.881e-01 Loss in Target Net: 1.348e+00
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2020-02-02 11:56:01 Iteration 50 Training Loss: 2.556e-01 Loss in Target Net: 5.997e-02
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2020-02-02 11:56:19 Iteration 100 Training Loss: 2.247e-01 Loss in Target Net: 5.536e-02
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2020-02-02 11:56:36 Iteration 150 Training Loss: 2.118e-01 Loss in Target Net: 4.661e-02
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2020-02-02 11:56:54 Iteration 200 Training Loss: 2.031e-01 Loss in Target Net: 4.320e-02
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2020-02-02 11:57:12 Iteration 250 Training Loss: 1.912e-01 Loss in Target Net: 4.305e-02
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2020-02-02 11:57:29 Iteration 300 Training Loss: 1.922e-01 Loss in Target Net: 3.905e-02
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2020-02-02 11:57:48 Iteration 350 Training Loss: 1.887e-01 Loss in Target Net: 4.083e-02
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2020-02-02 11:58:05 Iteration 400 Training Loss: 1.869e-01 Loss in Target Net: 4.597e-02
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2020-02-02 11:58:24 Iteration 450 Training Loss: 1.849e-01 Loss in Target Net: 4.310e-02
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2020-02-02 11:58:42 Iteration 500 Training Loss: 1.882e-01 Loss in Target Net: 4.186e-02
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2020-02-02 11:58:59 Iteration 550 Training Loss: 1.817e-01 Loss in Target Net: 4.157e-02
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2020-02-02 11:59:17 Iteration 600 Training Loss: 1.881e-01 Loss in Target Net: 3.895e-02
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2020-02-02 11:59:34 Iteration 650 Training Loss: 1.820e-01 Loss in Target Net: 4.397e-02
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2020-02-02 11:59:52 Iteration 700 Training Loss: 1.814e-01 Loss in Target Net: 4.759e-02
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2020-02-02 12:00:10 Iteration 750 Training Loss: 1.802e-01 Loss in Target Net: 3.934e-02
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2020-02-02 12:00:27 Iteration 800 Training Loss: 1.804e-01 Loss in Target Net: 3.981e-02
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2020-02-02 12:00:45 Iteration 850 Training Loss: 1.796e-01 Loss in Target Net: 3.885e-02
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2020-02-02 12:01:03 Iteration 900 Training Loss: 1.824e-01 Loss in Target Net: 3.903e-02
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2020-02-02 12:01:21 Iteration 950 Training Loss: 1.800e-01 Loss in Target Net: 3.529e-02
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2020-02-02 12:01:38 Iteration 1000 Training Loss: 1.784e-01 Loss in Target Net: 4.131e-02
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2020-02-02 12:01:55 Iteration 1050 Training Loss: 1.794e-01 Loss in Target Net: 4.094e-02
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2020-02-02 12:02:13 Iteration 1100 Training Loss: 1.799e-01 Loss in Target Net: 3.874e-02
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2020-02-02 12:02:31 Iteration 1150 Training Loss: 1.804e-01 Loss in Target Net: 3.157e-02
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2020-02-02 12:02:48 Iteration 1200 Training Loss: 1.753e-01 Loss in Target Net: 3.474e-02
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2020-02-02 12:03:06 Iteration 1250 Training Loss: 1.788e-01 Loss in Target Net: 3.218e-02
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2020-02-02 12:03:24 Iteration 1300 Training Loss: 1.758e-01 Loss in Target Net: 3.731e-02
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2020-02-02 12:03:43 Iteration 1350 Training Loss: 1.762e-01 Loss in Target Net: 3.722e-02
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2020-02-02 12:04:01 Iteration 1400 Training Loss: 1.749e-01 Loss in Target Net: 3.179e-02
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2020-02-02 12:04:19 Iteration 1450 Training Loss: 1.766e-01 Loss in Target Net: 3.737e-02
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2020-02-02 12:04:37 Iteration 1499 Training Loss: 1.758e-01 Loss in Target Net: 3.159e-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 12:04:46, Epoch 0, Iteration 7, loss 0.259 (0.411), acc 92.308 (91.800)
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2020-02-02 12:05:44, 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:[-1.1667101, 0.0063235834, -2.8431027, -0.19332719, -4.13888, -3.1460114, 3.3358514, -2.3149304, 11.408342, -0.52121174], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:06:44 Epoch 59, Val iteration 0, acc 92.000 (92.000)
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2020-02-02 12:06:51 Epoch 59, Val iteration 19, acc 94.000 (92.940)
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* Prec: 92.94000129699707
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 27
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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=28, 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/1500/28
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:09:35 Iteration 0 Training Loss: 1.017e+00 Loss in Target Net: 1.365e+00
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2020-02-02 12:09:53 Iteration 50 Training Loss: 2.144e-01 Loss in Target Net: 3.882e-02
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2020-02-02 12:10:11 Iteration 100 Training Loss: 1.898e-01 Loss in Target Net: 3.399e-02
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2020-02-02 12:10:28 Iteration 150 Training Loss: 1.823e-01 Loss in Target Net: 3.133e-02
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2020-02-02 12:10:46 Iteration 200 Training Loss: 1.709e-01 Loss in Target Net: 2.647e-02
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2020-02-02 12:11:04 Iteration 250 Training Loss: 1.724e-01 Loss in Target Net: 2.500e-02
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2020-02-02 12:11:24 Iteration 300 Training Loss: 1.670e-01 Loss in Target Net: 2.497e-02
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2020-02-02 12:11:43 Iteration 350 Training Loss: 1.631e-01 Loss in Target Net: 2.185e-02
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2020-02-02 12:12:01 Iteration 400 Training Loss: 1.653e-01 Loss in Target Net: 2.234e-02
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2020-02-02 12:12:18 Iteration 450 Training Loss: 1.678e-01 Loss in Target Net: 2.065e-02
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2020-02-02 12:12:35 Iteration 500 Training Loss: 1.644e-01 Loss in Target Net: 2.072e-02
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2020-02-02 12:12:55 Iteration 550 Training Loss: 1.653e-01 Loss in Target Net: 2.137e-02
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2020-02-02 12:13:15 Iteration 600 Training Loss: 1.610e-01 Loss in Target Net: 2.062e-02
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2020-02-02 12:13:33 Iteration 650 Training Loss: 1.633e-01 Loss in Target Net: 2.252e-02
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2020-02-02 12:13:52 Iteration 700 Training Loss: 1.636e-01 Loss in Target Net: 2.090e-02
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2020-02-02 12:14:10 Iteration 750 Training Loss: 1.599e-01 Loss in Target Net: 2.009e-02
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2020-02-02 12:14:28 Iteration 800 Training Loss: 1.620e-01 Loss in Target Net: 1.905e-02
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2020-02-02 12:14:47 Iteration 850 Training Loss: 1.618e-01 Loss in Target Net: 2.176e-02
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2020-02-02 12:15:06 Iteration 900 Training Loss: 1.641e-01 Loss in Target Net: 2.257e-02
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