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2020-02-03 03:14:09 Iteration 0 Training Loss: 9.837e-01 Loss in Target Net: 1.086e+00
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2020-02-03 03:14:37 Iteration 50 Training Loss: 2.480e-01 Loss in Target Net: 1.757e-01
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2020-02-03 03:15:09 Iteration 100 Training Loss: 2.222e-01 Loss in Target Net: 1.036e-01
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2020-02-03 03:15:46 Iteration 150 Training Loss: 2.080e-01 Loss in Target Net: 9.263e-02
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2020-02-03 03:16:18 Iteration 200 Training Loss: 2.098e-01 Loss in Target Net: 9.290e-02
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2020-02-03 03:16:43 Iteration 250 Training Loss: 1.977e-01 Loss in Target Net: 8.541e-02
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2020-02-03 03:17:21 Iteration 300 Training Loss: 1.890e-01 Loss in Target Net: 7.350e-02
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2020-02-03 03:17:53 Iteration 350 Training Loss: 1.934e-01 Loss in Target Net: 7.159e-02
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2020-02-03 03:18:24 Iteration 400 Training Loss: 1.918e-01 Loss in Target Net: 7.358e-02
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2020-02-03 03:18:51 Iteration 450 Training Loss: 1.916e-01 Loss in Target Net: 7.268e-02
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2020-02-03 03:19:20 Iteration 500 Training Loss: 1.867e-01 Loss in Target Net: 6.315e-02
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2020-02-03 03:19:56 Iteration 550 Training Loss: 1.820e-01 Loss in Target Net: 6.828e-02
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2020-02-03 03:20:28 Iteration 600 Training Loss: 1.866e-01 Loss in Target Net: 7.426e-02
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2020-02-03 03:20:59 Iteration 650 Training Loss: 1.867e-01 Loss in Target Net: 7.256e-02
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2020-02-03 03:21:30 Iteration 700 Training Loss: 1.888e-01 Loss in Target Net: 8.401e-02
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2020-02-03 03:22:05 Iteration 750 Training Loss: 1.899e-01 Loss in Target Net: 7.899e-02
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2020-02-03 03:22:38 Iteration 800 Training Loss: 1.858e-01 Loss in Target Net: 8.660e-02
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2020-02-03 03:23:10 Iteration 850 Training Loss: 1.817e-01 Loss in Target Net: 7.787e-02
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2020-02-03 03:23:50 Iteration 900 Training Loss: 1.813e-01 Loss in Target Net: 8.167e-02
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2020-02-03 03:24:34 Iteration 950 Training Loss: 1.811e-01 Loss in Target Net: 7.546e-02
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2020-02-03 03:25:14 Iteration 1000 Training Loss: 1.828e-01 Loss in Target Net: 6.384e-02
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2020-02-03 03:26:11 Iteration 1050 Training Loss: 1.788e-01 Loss in Target Net: 6.686e-02
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2020-02-03 03:26:56 Iteration 1100 Training Loss: 1.860e-01 Loss in Target Net: 8.555e-02
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2020-02-03 03:27:25 Iteration 1150 Training Loss: 1.816e-01 Loss in Target Net: 8.576e-02
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2020-02-03 03:28:12 Iteration 1200 Training Loss: 1.822e-01 Loss in Target Net: 7.057e-02
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2020-02-03 03:28:58 Iteration 1250 Training Loss: 1.796e-01 Loss in Target Net: 6.654e-02
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2020-02-03 03:29:36 Iteration 1300 Training Loss: 1.785e-01 Loss in Target Net: 7.578e-02
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2020-02-03 03:30:13 Iteration 1350 Training Loss: 1.814e-01 Loss in Target Net: 8.262e-02
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2020-02-03 03:30:54 Iteration 1400 Training Loss: 1.792e-01 Loss in Target Net: 7.724e-02
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2020-02-03 03:31:31 Iteration 1450 Training Loss: 1.796e-01 Loss in Target Net: 8.274e-02
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2020-02-03 03:32:12 Iteration 1499 Training Loss: 1.825e-01 Loss in Target Net: 7.106e-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 03:32:27, Epoch 0, Iteration 7, loss 0.544 (0.408), acc 80.769 (89.200)
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2020-02-03 03:33:58, 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:[-1.3354324, 0.5324408, -3.1962948, -0.701916, -3.4246354, -4.189211, 2.120729, -1.633366, 12.394517, -0.17748368], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 03:35:48 Epoch 59, Val iteration 0, acc 92.800 (92.800)
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2020-02-03 03:36:02 Epoch 59, Val iteration 19, acc 93.000 (93.450)
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* Prec: 93.45000190734864
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 18
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TARGET INDEX: 5
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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=6, 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/1500/6
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:00:45 Iteration 0 Training Loss: 9.968e-01 Loss in Target Net: 1.356e+00
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2020-02-02 11:01:03 Iteration 50 Training Loss: 2.215e-01 Loss in Target Net: 3.799e-02
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2020-02-02 11:01:22 Iteration 100 Training Loss: 1.939e-01 Loss in Target Net: 2.760e-02
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2020-02-02 11:01:41 Iteration 150 Training Loss: 1.793e-01 Loss in Target Net: 2.987e-02
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2020-02-02 11:02:01 Iteration 200 Training Loss: 1.783e-01 Loss in Target Net: 2.802e-02
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2020-02-02 11:02:20 Iteration 250 Training Loss: 1.711e-01 Loss in Target Net: 2.660e-02
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2020-02-02 11:02:37 Iteration 300 Training Loss: 1.713e-01 Loss in Target Net: 2.750e-02
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2020-02-02 11:02:55 Iteration 350 Training Loss: 1.688e-01 Loss in Target Net: 2.511e-02
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2020-02-02 11:03:15 Iteration 400 Training Loss: 1.672e-01 Loss in Target Net: 2.117e-02
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2020-02-02 11:03:33 Iteration 450 Training Loss: 1.650e-01 Loss in Target Net: 2.372e-02
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2020-02-02 11:03:52 Iteration 500 Training Loss: 1.644e-01 Loss in Target Net: 1.842e-02
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2020-02-02 11:04:10 Iteration 550 Training Loss: 1.662e-01 Loss in Target Net: 2.345e-02
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2020-02-02 11:04:30 Iteration 600 Training Loss: 1.658e-01 Loss in Target Net: 2.158e-02
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2020-02-02 11:04:50 Iteration 650 Training Loss: 1.640e-01 Loss in Target Net: 2.647e-02
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2020-02-02 11:05:07 Iteration 700 Training Loss: 1.629e-01 Loss in Target Net: 2.298e-02
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2020-02-02 11:05:27 Iteration 750 Training Loss: 1.633e-01 Loss in Target Net: 2.127e-02
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2020-02-02 11:05:45 Iteration 800 Training Loss: 1.610e-01 Loss in Target Net: 3.136e-02
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2020-02-02 11:06:03 Iteration 850 Training Loss: 1.604e-01 Loss in Target Net: 2.783e-02
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2020-02-02 11:06:22 Iteration 900 Training Loss: 1.645e-01 Loss in Target Net: 2.686e-02
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2020-02-02 11:06:42 Iteration 950 Training Loss: 1.586e-01 Loss in Target Net: 2.361e-02
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2020-02-02 11:07:01 Iteration 1000 Training Loss: 1.637e-01 Loss in Target Net: 1.800e-02
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2020-02-02 11:07:19 Iteration 1050 Training Loss: 1.614e-01 Loss in Target Net: 2.969e-02
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2020-02-02 11:07:37 Iteration 1100 Training Loss: 1.611e-01 Loss in Target Net: 2.908e-02
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2020-02-02 11:07:55 Iteration 1150 Training Loss: 1.624e-01 Loss in Target Net: 2.187e-02
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2020-02-02 11:08:12 Iteration 1200 Training Loss: 1.602e-01 Loss in Target Net: 2.202e-02
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2020-02-02 11:08:31 Iteration 1250 Training Loss: 1.588e-01 Loss in Target Net: 2.484e-02
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2020-02-02 11:08:48 Iteration 1300 Training Loss: 1.610e-01 Loss in Target Net: 2.478e-02
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2020-02-02 11:09:08 Iteration 1350 Training Loss: 1.634e-01 Loss in Target Net: 1.933e-02
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2020-02-02 11:09:25 Iteration 1400 Training Loss: 1.638e-01 Loss in Target Net: 2.384e-02
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2020-02-02 11:09:42 Iteration 1450 Training Loss: 1.608e-01 Loss in Target Net: 2.585e-02
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2020-02-02 11:09:59 Iteration 1499 Training Loss: 1.616e-01 Loss in Target Net: 2.864e-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 11:10:09, Epoch 0, Iteration 7, loss 0.261 (0.513), acc 90.385 (89.200)
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2020-02-02 11:11:07, 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:[-3.307035, -1.5874965, -3.5757701, -2.961432, 1.9610777, -2.1100307, 7.5310683, -2.4577487, 8.061215, -1.1465745], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:12:06 Epoch 59, Val iteration 0, acc 94.600 (94.600)
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2020-02-02 11:12:14 Epoch 59, Val iteration 19, acc 93.400 (93.380)
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* Prec: 93.38000221252442
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 6
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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=7, 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/1500/7
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:01:01 Iteration 0 Training Loss: 1.077e+00 Loss in Target Net: 1.336e+00
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2020-02-02 11:01:18 Iteration 50 Training Loss: 2.903e-01 Loss in Target Net: 2.231e-01
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