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2020-02-04 03:52:54 Iteration 1450 Training Loss: 1.540e-01 Loss in Target Net: 2.196e-02 |
2020-02-04 03:55:58 Iteration 1499 Training Loss: 1.518e-01 Loss in Target Net: 2.388e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:56:50, Epoch 0, Iteration 7, loss 0.497 (0.449), acc 84.615 (89.600) |
2020-02-04 04:01:39, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-2.2838938, -0.7446566, -2.534742, 2.5491066, -2.744793, -1.4588102, 6.514602, -2.3760998, 6.49521, -2.921016], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:06:58 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-02-04 04:07:48 Epoch 59, Val iteration 19, acc 92.800 (92.880) |
* Prec: 92.88000221252442 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 95 |
TARGET INDEX: 30 |
DPN92 0 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='15', 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=31, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-end2end/mean-3Repeat/1500/31 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:41:11 Iteration 0 Training Loss: 1.082e+00 Loss in Target Net: 1.433e+00 |
2020-02-04 02:44:24 Iteration 50 Training Loss: 2.322e-01 Loss in Target Net: 8.539e-02 |
2020-02-04 02:47:35 Iteration 100 Training Loss: 1.932e-01 Loss in Target Net: 6.220e-02 |
2020-02-04 02:50:46 Iteration 150 Training Loss: 1.803e-01 Loss in Target Net: 5.074e-02 |
2020-02-04 02:53:57 Iteration 200 Training Loss: 1.691e-01 Loss in Target Net: 4.661e-02 |
2020-02-04 02:57:09 Iteration 250 Training Loss: 1.660e-01 Loss in Target Net: 4.338e-02 |
2020-02-04 03:00:23 Iteration 300 Training Loss: 1.639e-01 Loss in Target Net: 4.298e-02 |
2020-02-04 03:03:36 Iteration 350 Training Loss: 1.596e-01 Loss in Target Net: 4.336e-02 |
2020-02-04 03:06:49 Iteration 400 Training Loss: 1.582e-01 Loss in Target Net: 3.914e-02 |
2020-02-04 03:10:01 Iteration 450 Training Loss: 1.567e-01 Loss in Target Net: 4.415e-02 |
2020-02-04 03:13:13 Iteration 500 Training Loss: 1.552e-01 Loss in Target Net: 4.310e-02 |
2020-02-04 03:16:25 Iteration 550 Training Loss: 1.565e-01 Loss in Target Net: 3.865e-02 |
2020-02-04 03:19:36 Iteration 600 Training Loss: 1.563e-01 Loss in Target Net: 4.069e-02 |
2020-02-04 03:22:48 Iteration 650 Training Loss: 1.535e-01 Loss in Target Net: 4.100e-02 |
2020-02-04 03:26:00 Iteration 700 Training Loss: 1.551e-01 Loss in Target Net: 4.077e-02 |
2020-02-04 03:29:13 Iteration 750 Training Loss: 1.528e-01 Loss in Target Net: 3.871e-02 |
2020-02-04 03:32:24 Iteration 800 Training Loss: 1.521e-01 Loss in Target Net: 4.604e-02 |
2020-02-04 03:35:36 Iteration 850 Training Loss: 1.494e-01 Loss in Target Net: 3.948e-02 |
2020-02-04 03:38:48 Iteration 900 Training Loss: 1.499e-01 Loss in Target Net: 3.853e-02 |
2020-02-04 03:41:59 Iteration 950 Training Loss: 1.491e-01 Loss in Target Net: 3.854e-02 |
2020-02-04 03:45:10 Iteration 1000 Training Loss: 1.499e-01 Loss in Target Net: 3.579e-02 |
2020-02-04 03:48:22 Iteration 1050 Training Loss: 1.504e-01 Loss in Target Net: 3.875e-02 |
2020-02-04 03:51:33 Iteration 1100 Training Loss: 1.532e-01 Loss in Target Net: 3.316e-02 |
2020-02-04 03:54:47 Iteration 1150 Training Loss: 1.516e-01 Loss in Target Net: 3.469e-02 |
2020-02-04 03:57:57 Iteration 1200 Training Loss: 1.527e-01 Loss in Target Net: 3.439e-02 |
2020-02-04 04:01:15 Iteration 1250 Training Loss: 1.522e-01 Loss in Target Net: 3.101e-02 |
2020-02-04 04:04:45 Iteration 1300 Training Loss: 1.479e-01 Loss in Target Net: 3.282e-02 |
2020-02-04 04:08:09 Iteration 1350 Training Loss: 1.478e-01 Loss in Target Net: 3.218e-02 |
2020-02-04 04:11:12 Iteration 1400 Training Loss: 1.491e-01 Loss in Target Net: 3.316e-02 |
2020-02-04 04:14:24 Iteration 1450 Training Loss: 1.507e-01 Loss in Target Net: 3.536e-02 |
2020-02-04 04:17:44 Iteration 1499 Training Loss: 1.520e-01 Loss in Target Net: 3.436e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:18:36, Epoch 0, Iteration 7, loss 0.570 (0.340), acc 86.538 (92.600) |
2020-02-04 04:23:26, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-2.7000961, -0.8826445, -2.2557364, -2.20502, 0.46437204, -1.9594953, 6.205583, -1.8337237, 6.649271, -1.231657], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:28:27 Epoch 59, Val iteration 0, acc 92.400 (92.400) |
2020-02-04 04:29:15 Epoch 59, Val iteration 19, acc 92.600 (92.560) |
* Prec: 92.56000175476075 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 31 |
DPN92 1 |
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=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=32, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-end2end/mean-3Repeat/1500/32 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:10:17 Iteration 0 Training Loss: 1.005e+00 Loss in Target Net: 1.417e+00 |
2020-02-04 04:13:22 Iteration 50 Training Loss: 2.315e-01 Loss in Target Net: 6.890e-02 |
2020-02-04 04:16:37 Iteration 100 Training Loss: 2.030e-01 Loss in Target Net: 6.721e-02 |
2020-02-04 04:20:01 Iteration 150 Training Loss: 1.902e-01 Loss in Target Net: 6.146e-02 |
2020-02-04 04:23:19 Iteration 200 Training Loss: 1.803e-01 Loss in Target Net: 4.881e-02 |
2020-02-04 04:26:26 Iteration 250 Training Loss: 1.810e-01 Loss in Target Net: 4.462e-02 |
2020-02-04 04:29:29 Iteration 300 Training Loss: 1.737e-01 Loss in Target Net: 4.735e-02 |
2020-02-04 04:32:35 Iteration 350 Training Loss: 1.810e-01 Loss in Target Net: 4.852e-02 |
2020-02-04 04:35:47 Iteration 400 Training Loss: 1.739e-01 Loss in Target Net: 5.528e-02 |
2020-02-04 04:38:54 Iteration 450 Training Loss: 1.676e-01 Loss in Target Net: 5.761e-02 |
2020-02-04 04:42:00 Iteration 500 Training Loss: 1.723e-01 Loss in Target Net: 7.608e-02 |
2020-02-04 04:45:07 Iteration 550 Training Loss: 1.666e-01 Loss in Target Net: 6.766e-02 |
2020-02-04 04:48:14 Iteration 600 Training Loss: 1.661e-01 Loss in Target Net: 5.179e-02 |
2020-02-04 04:51:21 Iteration 650 Training Loss: 1.620e-01 Loss in Target Net: 4.188e-02 |
2020-02-04 04:54:28 Iteration 700 Training Loss: 1.646e-01 Loss in Target Net: 3.922e-02 |
2020-02-04 04:57:35 Iteration 750 Training Loss: 1.710e-01 Loss in Target Net: 4.149e-02 |
2020-02-04 05:00:41 Iteration 800 Training Loss: 1.642e-01 Loss in Target Net: 4.249e-02 |
2020-02-04 05:03:48 Iteration 850 Training Loss: 1.647e-01 Loss in Target Net: 5.681e-02 |
2020-02-04 05:06:54 Iteration 900 Training Loss: 1.654e-01 Loss in Target Net: 4.833e-02 |
2020-02-04 05:10:00 Iteration 950 Training Loss: 1.650e-01 Loss in Target Net: 5.003e-02 |
2020-02-04 05:13:06 Iteration 1000 Training Loss: 1.611e-01 Loss in Target Net: 5.239e-02 |
2020-02-04 05:16:13 Iteration 1050 Training Loss: 1.634e-01 Loss in Target Net: 5.826e-02 |
2020-02-04 05:19:18 Iteration 1100 Training Loss: 1.621e-01 Loss in Target Net: 5.442e-02 |
2020-02-04 05:22:24 Iteration 1150 Training Loss: 1.625e-01 Loss in Target Net: 4.076e-02 |
2020-02-04 05:25:31 Iteration 1200 Training Loss: 1.662e-01 Loss in Target Net: 4.576e-02 |
2020-02-04 05:28:38 Iteration 1250 Training Loss: 1.601e-01 Loss in Target Net: 4.478e-02 |
2020-02-04 05:31:44 Iteration 1300 Training Loss: 1.634e-01 Loss in Target Net: 4.382e-02 |
2020-02-04 05:34:50 Iteration 1350 Training Loss: 1.628e-01 Loss in Target Net: 5.567e-02 |
2020-02-04 05:37:56 Iteration 1400 Training Loss: 1.598e-01 Loss in Target Net: 5.129e-02 |
2020-02-04 05:41:03 Iteration 1450 Training Loss: 1.617e-01 Loss in Target Net: 3.468e-02 |
2020-02-04 05:44:05 Iteration 1499 Training Loss: 1.658e-01 Loss in Target Net: 3.500e-02 |
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