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2020-02-04 05:46:15, Epoch 0, Iteration 7, loss 0.313 (0.376), acc 90.385 (91.000)
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2020-02-04 05:51:09, 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:[-4.750111, -2.1012425, -0.32250768, 4.5176034, 1.4305577, -1.7907432, 3.343412, -2.4452558, 5.2383866, -2.9265928], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-04 05:56:27 Epoch 59, Val iteration 0, acc 92.200 (92.200)
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2020-02-04 05:57:16 Epoch 59, Val iteration 19, acc 92.800 (92.620)
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* Prec: 92.62000122070313
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 94
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TARGET INDEX: 36
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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='5', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=37, 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-3Repeat/1500/37
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-04 04:12:22 Iteration 0 Training Loss: 9.784e-01 Loss in Target Net: 1.280e+00
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2020-02-04 04:15:41 Iteration 50 Training Loss: 2.044e-01 Loss in Target Net: 3.626e-02
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2020-02-04 04:19:09 Iteration 100 Training Loss: 1.760e-01 Loss in Target Net: 3.156e-02
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2020-02-04 04:22:35 Iteration 150 Training Loss: 1.625e-01 Loss in Target Net: 2.642e-02
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2020-02-04 04:25:54 Iteration 200 Training Loss: 1.556e-01 Loss in Target Net: 3.000e-02
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2020-02-04 04:29:08 Iteration 250 Training Loss: 1.561e-01 Loss in Target Net: 2.277e-02
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2020-02-04 04:32:23 Iteration 300 Training Loss: 1.518e-01 Loss in Target Net: 2.134e-02
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2020-02-04 04:35:38 Iteration 350 Training Loss: 1.473e-01 Loss in Target Net: 2.164e-02
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2020-02-04 04:38:55 Iteration 400 Training Loss: 1.513e-01 Loss in Target Net: 2.008e-02
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2020-02-04 04:42:13 Iteration 450 Training Loss: 1.481e-01 Loss in Target Net: 1.802e-02
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2020-02-04 04:45:29 Iteration 500 Training Loss: 1.468e-01 Loss in Target Net: 2.055e-02
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2020-02-04 04:48:47 Iteration 550 Training Loss: 1.450e-01 Loss in Target Net: 1.791e-02
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2020-02-04 04:52:02 Iteration 600 Training Loss: 1.453e-01 Loss in Target Net: 1.824e-02
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2020-02-04 04:55:20 Iteration 650 Training Loss: 1.433e-01 Loss in Target Net: 1.889e-02
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2020-02-04 04:58:38 Iteration 700 Training Loss: 1.451e-01 Loss in Target Net: 2.080e-02
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2020-02-04 05:01:51 Iteration 750 Training Loss: 1.423e-01 Loss in Target Net: 1.877e-02
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2020-02-04 05:05:06 Iteration 800 Training Loss: 1.429e-01 Loss in Target Net: 2.252e-02
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2020-02-04 05:08:19 Iteration 850 Training Loss: 1.423e-01 Loss in Target Net: 2.373e-02
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2020-02-04 05:11:33 Iteration 900 Training Loss: 1.416e-01 Loss in Target Net: 2.383e-02
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2020-02-04 05:14:48 Iteration 950 Training Loss: 1.397e-01 Loss in Target Net: 2.239e-02
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2020-02-04 05:18:04 Iteration 1000 Training Loss: 1.430e-01 Loss in Target Net: 2.266e-02
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2020-02-04 05:21:19 Iteration 1050 Training Loss: 1.418e-01 Loss in Target Net: 2.421e-02
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2020-02-04 05:24:33 Iteration 1100 Training Loss: 1.389e-01 Loss in Target Net: 2.262e-02
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2020-02-04 05:27:51 Iteration 1150 Training Loss: 1.414e-01 Loss in Target Net: 2.218e-02
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2020-02-04 05:31:09 Iteration 1200 Training Loss: 1.403e-01 Loss in Target Net: 2.123e-02
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2020-02-04 05:34:25 Iteration 1250 Training Loss: 1.398e-01 Loss in Target Net: 2.392e-02
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2020-02-04 05:37:43 Iteration 1300 Training Loss: 1.414e-01 Loss in Target Net: 2.160e-02
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2020-02-04 05:40:58 Iteration 1350 Training Loss: 1.403e-01 Loss in Target Net: 1.974e-02
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2020-02-04 05:44:12 Iteration 1400 Training Loss: 1.403e-01 Loss in Target Net: 2.370e-02
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2020-02-04 05:47:29 Iteration 1450 Training Loss: 1.385e-01 Loss in Target Net: 2.198e-02
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2020-02-04 05:50:52 Iteration 1499 Training Loss: 1.402e-01 Loss in Target Net: 2.415e-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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2020-02-04 05:51:44, Epoch 0, Iteration 7, loss 0.496 (0.426), acc 88.462 (90.200)
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2020-02-04 05:56:36, 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:8, Target's Score:[-0.012628521, 1.5304985, -4.282504, -4.5596776, -2.2347364, -1.1658498, 6.4409347, -2.3414278, 9.886551, -3.00808], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-04 06:01:31 Epoch 59, Val iteration 0, acc 93.000 (93.000)
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2020-02-04 06:02:17 Epoch 59, Val iteration 19, acc 93.200 (93.180)
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* Prec: 93.18000183105468
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 99
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TARGET INDEX: 37
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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='6', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=38, 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-3Repeat/1500/38
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-04 04:28:11 Iteration 0 Training Loss: 9.712e-01 Loss in Target Net: 1.241e+00
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2020-02-04 04:31:33 Iteration 50 Training Loss: 2.342e-01 Loss in Target Net: 7.867e-02
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2020-02-04 04:34:56 Iteration 100 Training Loss: 2.090e-01 Loss in Target Net: 6.910e-02
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2020-02-04 04:38:19 Iteration 150 Training Loss: 1.896e-01 Loss in Target Net: 5.789e-02
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2020-02-04 04:41:42 Iteration 200 Training Loss: 1.805e-01 Loss in Target Net: 4.450e-02
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2020-02-04 04:45:04 Iteration 250 Training Loss: 1.766e-01 Loss in Target Net: 4.751e-02
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2020-02-04 04:48:26 Iteration 300 Training Loss: 1.741e-01 Loss in Target Net: 4.287e-02
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2020-02-04 04:51:48 Iteration 350 Training Loss: 1.704e-01 Loss in Target Net: 4.993e-02
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2020-02-04 04:55:10 Iteration 400 Training Loss: 1.739e-01 Loss in Target Net: 5.020e-02
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2020-02-04 04:58:32 Iteration 450 Training Loss: 1.683e-01 Loss in Target Net: 4.048e-02
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2020-02-04 05:01:53 Iteration 500 Training Loss: 1.669e-01 Loss in Target Net: 4.687e-02
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2020-02-04 05:05:16 Iteration 550 Training Loss: 1.697e-01 Loss in Target Net: 4.116e-02
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2020-02-04 05:08:38 Iteration 600 Training Loss: 1.654e-01 Loss in Target Net: 4.668e-02
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2020-02-04 05:12:01 Iteration 650 Training Loss: 1.657e-01 Loss in Target Net: 3.278e-02
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2020-02-04 05:15:27 Iteration 700 Training Loss: 1.677e-01 Loss in Target Net: 3.161e-02
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2020-02-04 05:18:52 Iteration 750 Training Loss: 1.644e-01 Loss in Target Net: 2.961e-02
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2020-02-04 05:22:15 Iteration 800 Training Loss: 1.636e-01 Loss in Target Net: 3.343e-02
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2020-02-04 05:25:37 Iteration 850 Training Loss: 1.672e-01 Loss in Target Net: 3.214e-02
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2020-02-04 05:28:59 Iteration 900 Training Loss: 1.663e-01 Loss in Target Net: 2.951e-02
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2020-02-04 05:32:22 Iteration 950 Training Loss: 1.645e-01 Loss in Target Net: 3.671e-02
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2020-02-04 05:35:46 Iteration 1000 Training Loss: 1.637e-01 Loss in Target Net: 3.271e-02
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2020-02-04 05:39:09 Iteration 1050 Training Loss: 1.651e-01 Loss in Target Net: 3.050e-02
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2020-02-04 05:42:33 Iteration 1100 Training Loss: 1.644e-01 Loss in Target Net: 2.861e-02
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2020-02-04 05:45:53 Iteration 1150 Training Loss: 1.654e-01 Loss in Target Net: 3.266e-02
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2020-02-04 05:49:24 Iteration 1200 Training Loss: 1.651e-01 Loss in Target Net: 3.633e-02
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2020-02-04 05:53:06 Iteration 1250 Training Loss: 1.619e-01 Loss in Target Net: 3.243e-02
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2020-02-04 05:56:30 Iteration 1300 Training Loss: 1.641e-01 Loss in Target Net: 3.689e-02
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2020-02-04 05:59:36 Iteration 1350 Training Loss: 1.625e-01 Loss in Target Net: 2.732e-02
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2020-02-04 06:02:32 Iteration 1400 Training Loss: 1.611e-01 Loss in Target Net: 3.807e-02
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2020-02-04 06:05:23 Iteration 1450 Training Loss: 1.634e-01 Loss in Target Net: 3.164e-02
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2020-02-04 06:08:11 Iteration 1499 Training Loss: 1.611e-01 Loss in Target Net: 4.154e-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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2020-02-04 06:09:02, Epoch 0, Iteration 7, loss 0.245 (0.498), acc 92.308 (90.400)
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2020-02-04 06:13:56, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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