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1.13k
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 44
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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='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=45, 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/1500/45
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:56:26 Iteration 0 Training Loss: 9.480e-01 Loss in Target Net: 1.231e+00
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2020-02-02 12:56:45 Iteration 50 Training Loss: 2.355e-01 Loss in Target Net: 5.053e-02
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2020-02-02 12:57:03 Iteration 100 Training Loss: 2.043e-01 Loss in Target Net: 3.438e-02
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2020-02-02 12:57:20 Iteration 150 Training Loss: 1.989e-01 Loss in Target Net: 2.668e-02
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2020-02-02 12:57:37 Iteration 200 Training Loss: 1.954e-01 Loss in Target Net: 3.266e-02
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2020-02-02 12:57:54 Iteration 250 Training Loss: 1.865e-01 Loss in Target Net: 2.689e-02
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2020-02-02 12:58:12 Iteration 300 Training Loss: 1.856e-01 Loss in Target Net: 2.023e-02
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2020-02-02 12:58:30 Iteration 350 Training Loss: 1.830e-01 Loss in Target Net: 2.580e-02
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2020-02-02 12:58:46 Iteration 400 Training Loss: 1.834e-01 Loss in Target Net: 2.074e-02
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2020-02-02 12:59:04 Iteration 450 Training Loss: 1.826e-01 Loss in Target Net: 2.194e-02
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2020-02-02 12:59:21 Iteration 500 Training Loss: 1.810e-01 Loss in Target Net: 2.314e-02
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2020-02-02 12:59:41 Iteration 550 Training Loss: 1.827e-01 Loss in Target Net: 2.119e-02
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2020-02-02 12:59:58 Iteration 600 Training Loss: 1.793e-01 Loss in Target Net: 2.321e-02
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2020-02-02 13:00:14 Iteration 650 Training Loss: 1.758e-01 Loss in Target Net: 2.352e-02
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2020-02-02 13:00:32 Iteration 700 Training Loss: 1.802e-01 Loss in Target Net: 2.270e-02
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2020-02-02 13:00:51 Iteration 750 Training Loss: 1.810e-01 Loss in Target Net: 2.153e-02
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2020-02-02 13:01:09 Iteration 800 Training Loss: 1.766e-01 Loss in Target Net: 1.851e-02
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2020-02-02 13:01:28 Iteration 850 Training Loss: 1.745e-01 Loss in Target Net: 2.161e-02
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2020-02-02 13:01:46 Iteration 900 Training Loss: 1.778e-01 Loss in Target Net: 1.950e-02
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2020-02-02 13:02:05 Iteration 950 Training Loss: 1.770e-01 Loss in Target Net: 2.009e-02
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2020-02-02 13:02:23 Iteration 1000 Training Loss: 1.753e-01 Loss in Target Net: 2.034e-02
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2020-02-02 13:02:40 Iteration 1050 Training Loss: 1.767e-01 Loss in Target Net: 2.400e-02
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2020-02-02 13:02:57 Iteration 1100 Training Loss: 1.758e-01 Loss in Target Net: 2.195e-02
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2020-02-02 13:03:14 Iteration 1150 Training Loss: 1.747e-01 Loss in Target Net: 2.142e-02
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2020-02-02 13:03:32 Iteration 1200 Training Loss: 1.757e-01 Loss in Target Net: 1.844e-02
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2020-02-02 13:03:52 Iteration 1250 Training Loss: 1.731e-01 Loss in Target Net: 1.825e-02
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2020-02-02 13:04:10 Iteration 1300 Training Loss: 1.766e-01 Loss in Target Net: 2.001e-02
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2020-02-02 13:04:26 Iteration 1350 Training Loss: 1.728e-01 Loss in Target Net: 2.451e-02
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2020-02-02 13:04:44 Iteration 1400 Training Loss: 1.759e-01 Loss in Target Net: 1.738e-02
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2020-02-02 13:05:02 Iteration 1450 Training Loss: 1.726e-01 Loss in Target Net: 1.932e-02
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2020-02-02 13:05:21 Iteration 1499 Training Loss: 1.781e-01 Loss in Target Net: 1.657e-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 13:05:30, Epoch 0, Iteration 7, loss 0.473 (0.508), acc 84.615 (88.800)
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2020-02-02 13:06:28, 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:[-3.1372368, -1.2969221, -1.5759952, -1.9304343, -1.3986856, -3.103597, 7.206691, -4.0576963, 11.478147, -1.77881], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:07:28 Epoch 59, Val iteration 0, acc 93.800 (93.800)
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2020-02-02 13:07:35 Epoch 59, Val iteration 19, acc 93.000 (93.400)
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* Prec: 93.40000114440917
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 45
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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=45, 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/1500/45
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-03 06:31:26 Iteration 0 Training Loss: 9.468e-01 Loss in Target Net: 1.232e+00
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2020-02-03 06:31:44 Iteration 50 Training Loss: 2.397e-01 Loss in Target Net: 4.235e-02
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2020-02-03 06:32:03 Iteration 100 Training Loss: 2.104e-01 Loss in Target Net: 3.248e-02
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2020-02-03 06:32:22 Iteration 150 Training Loss: 1.999e-01 Loss in Target Net: 2.886e-02
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2020-02-03 06:32:40 Iteration 200 Training Loss: 1.910e-01 Loss in Target Net: 3.228e-02
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2020-02-03 06:32:58 Iteration 250 Training Loss: 1.892e-01 Loss in Target Net: 2.969e-02
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2020-02-03 06:33:15 Iteration 300 Training Loss: 1.863e-01 Loss in Target Net: 2.725e-02
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2020-02-03 06:33:32 Iteration 350 Training Loss: 1.876e-01 Loss in Target Net: 2.790e-02
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2020-02-03 06:33:49 Iteration 400 Training Loss: 1.827e-01 Loss in Target Net: 2.883e-02
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2020-02-03 06:34:07 Iteration 450 Training Loss: 1.802e-01 Loss in Target Net: 2.713e-02
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2020-02-03 06:34:26 Iteration 500 Training Loss: 1.814e-01 Loss in Target Net: 2.724e-02
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2020-02-03 06:34:44 Iteration 550 Training Loss: 1.813e-01 Loss in Target Net: 2.288e-02
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2020-02-03 06:35:01 Iteration 600 Training Loss: 1.750e-01 Loss in Target Net: 2.516e-02
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2020-02-03 06:35:19 Iteration 650 Training Loss: 1.819e-01 Loss in Target Net: 2.518e-02
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2020-02-03 06:35:37 Iteration 700 Training Loss: 1.798e-01 Loss in Target Net: 2.631e-02
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2020-02-03 06:35:55 Iteration 750 Training Loss: 1.780e-01 Loss in Target Net: 2.411e-02
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2020-02-03 06:36:12 Iteration 800 Training Loss: 1.852e-01 Loss in Target Net: 2.058e-02
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2020-02-03 06:36:29 Iteration 850 Training Loss: 1.770e-01 Loss in Target Net: 2.164e-02
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2020-02-03 06:36:46 Iteration 900 Training Loss: 1.760e-01 Loss in Target Net: 2.199e-02
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2020-02-03 06:37:02 Iteration 950 Training Loss: 1.750e-01 Loss in Target Net: 2.694e-02
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2020-02-03 06:37:19 Iteration 1000 Training Loss: 1.759e-01 Loss in Target Net: 2.142e-02
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2020-02-03 06:37:36 Iteration 1050 Training Loss: 1.762e-01 Loss in Target Net: 1.967e-02
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2020-02-03 06:37:53 Iteration 1100 Training Loss: 1.752e-01 Loss in Target Net: 2.079e-02
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2020-02-03 06:38:10 Iteration 1150 Training Loss: 1.768e-01 Loss in Target Net: 1.932e-02
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2020-02-03 06:38:26 Iteration 1200 Training Loss: 1.738e-01 Loss in Target Net: 2.311e-02
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2020-02-03 06:38:43 Iteration 1250 Training Loss: 1.739e-01 Loss in Target Net: 2.053e-02
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2020-02-03 06:39:00 Iteration 1300 Training Loss: 1.755e-01 Loss in Target Net: 1.909e-02
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2020-02-03 06:39:18 Iteration 1350 Training Loss: 1.705e-01 Loss in Target Net: 1.982e-02
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2020-02-03 06:39:36 Iteration 1400 Training Loss: 1.747e-01 Loss in Target Net: 2.139e-02
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2020-02-03 06:39:55 Iteration 1450 Training Loss: 1.791e-01 Loss in Target Net: 2.105e-02
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2020-02-03 06:40:14 Iteration 1499 Training Loss: 1.730e-01 Loss in Target Net: 2.058e-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 06:40:24, Epoch 0, Iteration 7, loss 0.257 (0.480), acc 94.231 (90.200)
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2020-02-03 06:41:22, 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:6, Target's Score:[-3.0003402, -0.4758992, 0.121072344, -0.40876716, -1.4154296, -1.5748805, 8.27839, -4.2122808, 6.5380635, -3.3534665], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 06:42:21 Epoch 59, Val iteration 0, acc 92.800 (92.800)
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2020-02-03 06:42:29 Epoch 59, Val iteration 19, acc 93.400 (92.570)
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* Prec: 92.57000198364258
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--------
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------SUMMARY------
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