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1.13k
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:41:58, Epoch 0, Iteration 7, loss 0.450 (0.465), acc 84.615 (90.600)
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2020-02-02 12:42:56, 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:[-1.9024915, -0.30645552, -4.0061755, -0.41198456, -2.4350576, -2.9030013, 5.270306, -1.9562314, 9.95247, -0.91891956], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:43:56 Epoch 59, Val iteration 0, acc 92.800 (92.800)
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2020-02-02 12:44:03 Epoch 59, Val iteration 19, acc 91.800 (92.500)
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* Prec: 92.50000114440918
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 38
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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=39, 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/1500/39
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:30:14 Iteration 0 Training Loss: 1.080e+00 Loss in Target Net: 1.582e+00
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2020-02-02 12:30:33 Iteration 50 Training Loss: 2.630e-01 Loss in Target Net: 1.011e-01
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2020-02-02 12:30:50 Iteration 100 Training Loss: 2.276e-01 Loss in Target Net: 7.025e-02
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2020-02-02 12:31:08 Iteration 150 Training Loss: 2.184e-01 Loss in Target Net: 1.021e-01
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2020-02-02 12:31:27 Iteration 200 Training Loss: 2.210e-01 Loss in Target Net: 1.393e-01
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2020-02-02 12:31:45 Iteration 250 Training Loss: 2.119e-01 Loss in Target Net: 1.125e-01
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2020-02-02 12:32:04 Iteration 300 Training Loss: 2.018e-01 Loss in Target Net: 1.104e-01
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2020-02-02 12:32:22 Iteration 350 Training Loss: 2.012e-01 Loss in Target Net: 1.357e-01
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2020-02-02 12:32:40 Iteration 400 Training Loss: 2.037e-01 Loss in Target Net: 1.762e-01
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2020-02-02 12:32:59 Iteration 450 Training Loss: 2.035e-01 Loss in Target Net: 7.592e-02
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2020-02-02 12:33:16 Iteration 500 Training Loss: 2.028e-01 Loss in Target Net: 6.401e-02
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2020-02-02 12:33:33 Iteration 550 Training Loss: 2.000e-01 Loss in Target Net: 6.807e-02
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2020-02-02 12:33:51 Iteration 600 Training Loss: 2.002e-01 Loss in Target Net: 7.531e-02
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2020-02-02 12:34:09 Iteration 650 Training Loss: 1.948e-01 Loss in Target Net: 8.759e-02
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2020-02-02 12:34:26 Iteration 700 Training Loss: 2.043e-01 Loss in Target Net: 7.160e-02
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2020-02-02 12:34:43 Iteration 750 Training Loss: 1.984e-01 Loss in Target Net: 8.085e-02
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2020-02-02 12:35:00 Iteration 800 Training Loss: 1.892e-01 Loss in Target Net: 8.303e-02
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2020-02-02 12:35:17 Iteration 850 Training Loss: 1.929e-01 Loss in Target Net: 1.142e-01
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2020-02-02 12:35:33 Iteration 900 Training Loss: 1.999e-01 Loss in Target Net: 9.688e-02
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2020-02-02 12:35:52 Iteration 950 Training Loss: 1.910e-01 Loss in Target Net: 8.124e-02
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2020-02-02 12:36:09 Iteration 1000 Training Loss: 1.897e-01 Loss in Target Net: 8.669e-02
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2020-02-02 12:36:25 Iteration 1050 Training Loss: 1.869e-01 Loss in Target Net: 1.063e-01
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2020-02-02 12:36:41 Iteration 1100 Training Loss: 1.930e-01 Loss in Target Net: 7.481e-02
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2020-02-02 12:36:57 Iteration 1150 Training Loss: 1.901e-01 Loss in Target Net: 8.796e-02
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2020-02-02 12:37:13 Iteration 1200 Training Loss: 1.984e-01 Loss in Target Net: 1.214e-01
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2020-02-02 12:37:28 Iteration 1250 Training Loss: 1.895e-01 Loss in Target Net: 9.998e-02
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2020-02-02 12:37:44 Iteration 1300 Training Loss: 1.870e-01 Loss in Target Net: 7.415e-02
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2020-02-02 12:38:00 Iteration 1350 Training Loss: 1.924e-01 Loss in Target Net: 9.781e-02
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2020-02-02 12:38:16 Iteration 1400 Training Loss: 1.903e-01 Loss in Target Net: 1.014e-01
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2020-02-02 12:38:33 Iteration 1450 Training Loss: 1.937e-01 Loss in Target Net: 1.043e-01
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2020-02-02 12:38:49 Iteration 1499 Training Loss: 1.920e-01 Loss in Target Net: 9.500e-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 12:38:58, Epoch 0, Iteration 7, loss 0.222 (0.459), acc 92.308 (90.000)
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2020-02-02 12:39:56, 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:[-4.3728056, -1.5583879, 0.0054999227, 3.5164065, 1.261999, -0.19929078, 2.8335824, -1.7410582, 2.837433, -2.3591387], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:40:55 Epoch 59, Val iteration 0, acc 92.000 (92.000)
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2020-02-02 12:41:02 Epoch 59, Val iteration 19, acc 92.600 (92.560)
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* Prec: 92.56000137329102
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 39
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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=4, 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/1500/4
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:00:30 Iteration 0 Training Loss: 1.019e+00 Loss in Target Net: 1.318e+00
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2020-02-02 11:00:49 Iteration 50 Training Loss: 2.239e-01 Loss in Target Net: 4.466e-02
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2020-02-02 11:01:06 Iteration 100 Training Loss: 1.982e-01 Loss in Target Net: 3.689e-02
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2020-02-02 11:01:25 Iteration 150 Training Loss: 1.866e-01 Loss in Target Net: 2.967e-02
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2020-02-02 11:01:43 Iteration 200 Training Loss: 1.835e-01 Loss in Target Net: 2.930e-02
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2020-02-02 11:02:02 Iteration 250 Training Loss: 1.793e-01 Loss in Target Net: 2.632e-02
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2020-02-02 11:02:21 Iteration 300 Training Loss: 1.737e-01 Loss in Target Net: 2.509e-02
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2020-02-02 11:02:42 Iteration 350 Training Loss: 1.707e-01 Loss in Target Net: 2.327e-02
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2020-02-02 11:03:01 Iteration 400 Training Loss: 1.678e-01 Loss in Target Net: 2.210e-02
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2020-02-02 11:03:19 Iteration 450 Training Loss: 1.678e-01 Loss in Target Net: 1.972e-02
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2020-02-02 11:03:39 Iteration 500 Training Loss: 1.701e-01 Loss in Target Net: 1.885e-02
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2020-02-02 11:04:00 Iteration 550 Training Loss: 1.704e-01 Loss in Target Net: 2.068e-02
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2020-02-02 11:04:21 Iteration 600 Training Loss: 1.645e-01 Loss in Target Net: 2.104e-02
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2020-02-02 11:04:40 Iteration 650 Training Loss: 1.658e-01 Loss in Target Net: 1.916e-02
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2020-02-02 11:04:58 Iteration 700 Training Loss: 1.682e-01 Loss in Target Net: 2.028e-02
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2020-02-02 11:05:19 Iteration 750 Training Loss: 1.674e-01 Loss in Target Net: 1.820e-02
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2020-02-02 11:05:38 Iteration 800 Training Loss: 1.677e-01 Loss in Target Net: 2.078e-02
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2020-02-02 11:05:58 Iteration 850 Training Loss: 1.664e-01 Loss in Target Net: 2.070e-02
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2020-02-02 11:06:19 Iteration 900 Training Loss: 1.699e-01 Loss in Target Net: 1.973e-02
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2020-02-02 11:06:40 Iteration 950 Training Loss: 1.655e-01 Loss in Target Net: 2.118e-02
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2020-02-02 11:07:00 Iteration 1000 Training Loss: 1.644e-01 Loss in Target Net: 2.179e-02
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2020-02-02 11:07:18 Iteration 1050 Training Loss: 1.625e-01 Loss in Target Net: 1.936e-02
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2020-02-02 11:07:37 Iteration 1100 Training Loss: 1.615e-01 Loss in Target Net: 1.913e-02
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2020-02-02 11:07:55 Iteration 1150 Training Loss: 1.649e-01 Loss in Target Net: 2.109e-02
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2020-02-02 11:08:14 Iteration 1200 Training Loss: 1.625e-01 Loss in Target Net: 2.415e-02
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2020-02-02 11:08:35 Iteration 1250 Training Loss: 1.621e-01 Loss in Target Net: 1.857e-02
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2020-02-02 11:08:55 Iteration 1300 Training Loss: 1.629e-01 Loss in Target Net: 2.347e-02
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2020-02-02 11:09:15 Iteration 1350 Training Loss: 1.599e-01 Loss in Target Net: 2.178e-02
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2020-02-02 11:09:35 Iteration 1400 Training Loss: 1.614e-01 Loss in Target Net: 1.963e-02
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2020-02-02 11:09:53 Iteration 1450 Training Loss: 1.638e-01 Loss in Target Net: 2.156e-02
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2020-02-02 11:10:14 Iteration 1499 Training Loss: 1.616e-01 Loss in Target Net: 1.989e-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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