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2020-02-02 11:01:35 Iteration 100 Training Loss: 2.634e-01 Loss in Target Net: 1.451e-01
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2020-02-02 11:01:52 Iteration 150 Training Loss: 2.495e-01 Loss in Target Net: 2.252e-01
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2020-02-02 11:02:08 Iteration 200 Training Loss: 2.405e-01 Loss in Target Net: 1.584e-01
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2020-02-02 11:02:25 Iteration 250 Training Loss: 2.356e-01 Loss in Target Net: 1.421e-01
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2020-02-02 11:02:42 Iteration 300 Training Loss: 2.323e-01 Loss in Target Net: 1.760e-01
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2020-02-02 11:02:58 Iteration 350 Training Loss: 2.282e-01 Loss in Target Net: 1.300e-01
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2020-02-02 11:03:15 Iteration 400 Training Loss: 2.249e-01 Loss in Target Net: 1.224e-01
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2020-02-02 11:03:32 Iteration 450 Training Loss: 2.266e-01 Loss in Target Net: 1.326e-01
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2020-02-02 11:03:49 Iteration 500 Training Loss: 2.255e-01 Loss in Target Net: 1.552e-01
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2020-02-02 11:04:06 Iteration 550 Training Loss: 2.289e-01 Loss in Target Net: 1.432e-01
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2020-02-02 11:04:23 Iteration 600 Training Loss: 2.152e-01 Loss in Target Net: 1.638e-01
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2020-02-02 11:04:40 Iteration 650 Training Loss: 2.236e-01 Loss in Target Net: 9.783e-02
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2020-02-02 11:04:57 Iteration 700 Training Loss: 2.236e-01 Loss in Target Net: 9.957e-02
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2020-02-02 11:05:14 Iteration 750 Training Loss: 2.155e-01 Loss in Target Net: 1.214e-01
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2020-02-02 11:05:31 Iteration 800 Training Loss: 2.151e-01 Loss in Target Net: 1.189e-01
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2020-02-02 11:05:48 Iteration 850 Training Loss: 2.187e-01 Loss in Target Net: 1.169e-01
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2020-02-02 11:06:05 Iteration 900 Training Loss: 2.155e-01 Loss in Target Net: 9.765e-02
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2020-02-02 11:06:21 Iteration 950 Training Loss: 2.164e-01 Loss in Target Net: 1.124e-01
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2020-02-02 11:06:40 Iteration 1000 Training Loss: 2.149e-01 Loss in Target Net: 1.390e-01
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2020-02-02 11:06:57 Iteration 1050 Training Loss: 2.244e-01 Loss in Target Net: 1.015e-01
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2020-02-02 11:07:14 Iteration 1100 Training Loss: 2.162e-01 Loss in Target Net: 1.350e-01
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2020-02-02 11:07:30 Iteration 1150 Training Loss: 2.103e-01 Loss in Target Net: 9.875e-02
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2020-02-02 11:07:48 Iteration 1200 Training Loss: 2.167e-01 Loss in Target Net: 9.400e-02
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2020-02-02 11:08:05 Iteration 1250 Training Loss: 2.072e-01 Loss in Target Net: 1.119e-01
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2020-02-02 11:08:22 Iteration 1300 Training Loss: 2.256e-01 Loss in Target Net: 1.199e-01
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2020-02-02 11:08:38 Iteration 1350 Training Loss: 2.069e-01 Loss in Target Net: 1.182e-01
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2020-02-02 11:08:56 Iteration 1400 Training Loss: 2.196e-01 Loss in Target Net: 1.013e-01
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2020-02-02 11:09:13 Iteration 1450 Training Loss: 2.142e-01 Loss in Target Net: 1.340e-01
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2020-02-02 11:09:29 Iteration 1499 Training Loss: 2.109e-01 Loss in Target Net: 8.946e-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 11:09:38, Epoch 0, Iteration 7, loss 0.472 (0.458), acc 86.538 (89.400)
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2020-02-02 11:10:36, 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:3, Target's Score:[-3.1226306, -2.969831, -0.012885735, 9.902816, -1.6195971, -2.2582252, 0.5372275, -1.7481483, 2.9402888, -1.264657], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:11:35 Epoch 59, Val iteration 0, acc 92.200 (92.200)
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2020-02-02 11:11:43 Epoch 59, Val iteration 19, acc 93.000 (92.890)
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* Prec: 92.89000129699707
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 7
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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='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=8, 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/1500/8
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:12:43 Iteration 0 Training Loss: 9.304e-01 Loss in Target Net: 1.131e+00
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2020-02-02 11:12:59 Iteration 50 Training Loss: 2.659e-01 Loss in Target Net: 1.634e-01
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2020-02-02 11:13:15 Iteration 100 Training Loss: 2.382e-01 Loss in Target Net: 1.211e-01
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2020-02-02 11:13:30 Iteration 150 Training Loss: 2.304e-01 Loss in Target Net: 9.449e-02
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2020-02-02 11:13:46 Iteration 200 Training Loss: 2.167e-01 Loss in Target Net: 8.944e-02
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2020-02-02 11:14:02 Iteration 250 Training Loss: 2.100e-01 Loss in Target Net: 8.609e-02
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2020-02-02 11:14:18 Iteration 300 Training Loss: 2.152e-01 Loss in Target Net: 7.823e-02
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2020-02-02 11:14:35 Iteration 350 Training Loss: 2.058e-01 Loss in Target Net: 7.688e-02
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2020-02-02 11:14:51 Iteration 400 Training Loss: 2.192e-01 Loss in Target Net: 1.127e-01
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2020-02-02 11:15:07 Iteration 450 Training Loss: 2.079e-01 Loss in Target Net: 8.635e-02
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2020-02-02 11:15:23 Iteration 500 Training Loss: 2.031e-01 Loss in Target Net: 7.238e-02
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2020-02-02 11:15:39 Iteration 550 Training Loss: 2.047e-01 Loss in Target Net: 7.911e-02
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2020-02-02 11:15:56 Iteration 600 Training Loss: 2.086e-01 Loss in Target Net: 7.108e-02
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2020-02-02 11:16:12 Iteration 650 Training Loss: 2.045e-01 Loss in Target Net: 8.422e-02
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2020-02-02 11:16:28 Iteration 700 Training Loss: 1.993e-01 Loss in Target Net: 6.964e-02
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2020-02-02 11:16:44 Iteration 750 Training Loss: 2.021e-01 Loss in Target Net: 7.853e-02
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2020-02-02 11:17:01 Iteration 800 Training Loss: 2.039e-01 Loss in Target Net: 8.564e-02
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2020-02-02 11:17:17 Iteration 850 Training Loss: 2.015e-01 Loss in Target Net: 7.953e-02
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2020-02-02 11:17:33 Iteration 900 Training Loss: 2.022e-01 Loss in Target Net: 8.184e-02
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2020-02-02 11:17:49 Iteration 950 Training Loss: 1.989e-01 Loss in Target Net: 8.524e-02
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2020-02-02 11:18:05 Iteration 1000 Training Loss: 1.981e-01 Loss in Target Net: 7.107e-02
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2020-02-02 11:18:22 Iteration 1050 Training Loss: 2.000e-01 Loss in Target Net: 7.394e-02
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2020-02-02 11:18:38 Iteration 1100 Training Loss: 2.010e-01 Loss in Target Net: 7.666e-02
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2020-02-02 11:18:54 Iteration 1150 Training Loss: 1.950e-01 Loss in Target Net: 6.455e-02
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2020-02-02 11:19:11 Iteration 1200 Training Loss: 1.958e-01 Loss in Target Net: 8.366e-02
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2020-02-02 11:19:27 Iteration 1250 Training Loss: 1.998e-01 Loss in Target Net: 6.043e-02
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2020-02-02 11:19:43 Iteration 1300 Training Loss: 1.988e-01 Loss in Target Net: 9.030e-02
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2020-02-02 11:20:00 Iteration 1350 Training Loss: 1.997e-01 Loss in Target Net: 5.905e-02
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2020-02-02 11:20:17 Iteration 1400 Training Loss: 1.985e-01 Loss in Target Net: 6.149e-02
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2020-02-02 11:20:34 Iteration 1450 Training Loss: 1.963e-01 Loss in Target Net: 6.438e-02
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2020-02-02 11:20:50 Iteration 1499 Training Loss: 1.945e-01 Loss in Target Net: 6.880e-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 11:21:00, Epoch 0, Iteration 7, loss 0.402 (0.398), acc 88.462 (90.400)
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2020-02-02 11:21:57, 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:[2.1514986, -0.7304404, -2.3261886, -1.469438, -3.1206405, -1.7265685, 3.8976479, -2.6919205, 7.8077035, -1.4601378], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:22:57 Epoch 59, Val iteration 0, acc 92.200 (92.200)
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2020-02-02 11:23:05 Epoch 59, Val iteration 19, acc 92.000 (92.830)
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* Prec: 92.83000106811524
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 8
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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=9, 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/1500/9
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:12:19 Iteration 0 Training Loss: 1.027e+00 Loss in Target Net: 1.404e+00
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2020-02-02 11:12:37 Iteration 50 Training Loss: 2.591e-01 Loss in Target Net: 5.049e-02
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2020-02-02 11:12:54 Iteration 100 Training Loss: 2.219e-01 Loss in Target Net: 3.164e-02
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2020-02-02 11:13:13 Iteration 150 Training Loss: 2.087e-01 Loss in Target Net: 3.141e-02
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