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Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 05:45:00, Epoch 0, Iteration 7, loss 0.388 (0.492), acc 90.385 (91.000) |
2020-02-04 05:49:52, 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:[-4.1015687, -2.1437633, 0.97200614, 3.9197505, -0.42145526, -1.775608, 5.246875, -2.9248195, 3.8983781, -2.2865875], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 05:55:12 Epoch 59, Val iteration 0, acc 92.200 (92.200) |
2020-02-04 05:56:01 Epoch 59, Val iteration 19, acc 93.000 (93.090) |
* Prec: 93.09000129699707 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 94 |
TARGET INDEX: 32 |
DPN92 0 |
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=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=33, 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/1500/33 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:11:56 Iteration 0 Training Loss: 1.027e+00 Loss in Target Net: 1.295e+00 |
2020-02-04 04:15:09 Iteration 50 Training Loss: 2.369e-01 Loss in Target Net: 5.358e-02 |
2020-02-04 04:18:35 Iteration 100 Training Loss: 2.025e-01 Loss in Target Net: 4.139e-02 |
2020-02-04 04:21:59 Iteration 150 Training Loss: 1.875e-01 Loss in Target Net: 4.277e-02 |
2020-02-04 04:25:15 Iteration 200 Training Loss: 1.800e-01 Loss in Target Net: 4.348e-02 |
2020-02-04 04:28:23 Iteration 250 Training Loss: 1.763e-01 Loss in Target Net: 3.404e-02 |
2020-02-04 04:31:32 Iteration 300 Training Loss: 1.707e-01 Loss in Target Net: 2.933e-02 |
2020-02-04 04:34:42 Iteration 350 Training Loss: 1.689e-01 Loss in Target Net: 2.505e-02 |
2020-02-04 04:37:53 Iteration 400 Training Loss: 1.669e-01 Loss in Target Net: 2.506e-02 |
2020-02-04 04:41:03 Iteration 450 Training Loss: 1.660e-01 Loss in Target Net: 3.119e-02 |
2020-02-04 04:44:15 Iteration 500 Training Loss: 1.659e-01 Loss in Target Net: 2.368e-02 |
2020-02-04 04:47:27 Iteration 550 Training Loss: 1.660e-01 Loss in Target Net: 3.151e-02 |
2020-02-04 04:50:38 Iteration 600 Training Loss: 1.635e-01 Loss in Target Net: 2.567e-02 |
2020-02-04 04:53:49 Iteration 650 Training Loss: 1.625e-01 Loss in Target Net: 2.134e-02 |
2020-02-04 04:56:59 Iteration 700 Training Loss: 1.634e-01 Loss in Target Net: 3.096e-02 |
2020-02-04 05:00:09 Iteration 750 Training Loss: 1.613e-01 Loss in Target Net: 2.926e-02 |
2020-02-04 05:03:20 Iteration 800 Training Loss: 1.590e-01 Loss in Target Net: 3.290e-02 |
2020-02-04 05:06:30 Iteration 850 Training Loss: 1.620e-01 Loss in Target Net: 3.677e-02 |
2020-02-04 05:09:41 Iteration 900 Training Loss: 1.609e-01 Loss in Target Net: 2.905e-02 |
2020-02-04 05:12:53 Iteration 950 Training Loss: 1.616e-01 Loss in Target Net: 3.191e-02 |
2020-02-04 05:16:04 Iteration 1000 Training Loss: 1.570e-01 Loss in Target Net: 2.649e-02 |
2020-02-04 05:19:14 Iteration 1050 Training Loss: 1.576e-01 Loss in Target Net: 2.971e-02 |
2020-02-04 05:22:25 Iteration 1100 Training Loss: 1.609e-01 Loss in Target Net: 2.585e-02 |
2020-02-04 05:25:35 Iteration 1150 Training Loss: 1.602e-01 Loss in Target Net: 2.504e-02 |
2020-02-04 05:28:47 Iteration 1200 Training Loss: 1.589e-01 Loss in Target Net: 2.468e-02 |
2020-02-04 05:31:57 Iteration 1250 Training Loss: 1.592e-01 Loss in Target Net: 3.000e-02 |
2020-02-04 05:35:08 Iteration 1300 Training Loss: 1.562e-01 Loss in Target Net: 2.831e-02 |
2020-02-04 05:38:19 Iteration 1350 Training Loss: 1.584e-01 Loss in Target Net: 2.721e-02 |
2020-02-04 05:41:30 Iteration 1400 Training Loss: 1.558e-01 Loss in Target Net: 2.523e-02 |
2020-02-04 05:44:38 Iteration 1450 Training Loss: 1.568e-01 Loss in Target Net: 2.761e-02 |
2020-02-04 05:47:48 Iteration 1499 Training Loss: 1.544e-01 Loss in Target Net: 3.070e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 05:48:41, Epoch 0, Iteration 7, loss 0.601 (0.465), acc 80.769 (89.600) |
2020-02-04 05:53:37, 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:[-1.2230455, -1.2815964, 1.3387787, -0.225399, -3.2452993, -5.284042, 8.59877, -2.4003701, 5.8602, -1.4229021], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 05:58:44 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-02-04 05:59:31 Epoch 59, Val iteration 19, acc 92.600 (92.770) |
* Prec: 92.77000122070312 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 96 |
TARGET INDEX: 33 |
DPN92 0 |
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=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=34, 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/1500/34 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:09:35 Iteration 0 Training Loss: 1.043e+00 Loss in Target Net: 1.366e+00 |
2020-02-04 04:12:31 Iteration 50 Training Loss: 2.376e-01 Loss in Target Net: 9.124e-02 |
2020-02-04 04:15:41 Iteration 100 Training Loss: 2.090e-01 Loss in Target Net: 8.065e-02 |
2020-02-04 04:19:03 Iteration 150 Training Loss: 1.943e-01 Loss in Target Net: 7.672e-02 |
2020-02-04 04:22:24 Iteration 200 Training Loss: 1.877e-01 Loss in Target Net: 6.504e-02 |
2020-02-04 04:25:34 Iteration 250 Training Loss: 1.824e-01 Loss in Target Net: 5.789e-02 |
2020-02-04 04:28:33 Iteration 300 Training Loss: 1.799e-01 Loss in Target Net: 6.038e-02 |
2020-02-04 04:31:37 Iteration 350 Training Loss: 1.774e-01 Loss in Target Net: 5.982e-02 |
2020-02-04 04:34:42 Iteration 400 Training Loss: 1.751e-01 Loss in Target Net: 6.201e-02 |
2020-02-04 04:37:49 Iteration 450 Training Loss: 1.782e-01 Loss in Target Net: 4.429e-02 |
2020-02-04 04:40:55 Iteration 500 Training Loss: 1.749e-01 Loss in Target Net: 5.176e-02 |
2020-02-04 04:44:01 Iteration 550 Training Loss: 1.723e-01 Loss in Target Net: 5.353e-02 |
2020-02-04 04:47:05 Iteration 600 Training Loss: 1.700e-01 Loss in Target Net: 4.952e-02 |
2020-02-04 04:50:09 Iteration 650 Training Loss: 1.733e-01 Loss in Target Net: 5.010e-02 |
2020-02-04 04:53:16 Iteration 700 Training Loss: 1.690e-01 Loss in Target Net: 4.314e-02 |
2020-02-04 04:56:21 Iteration 750 Training Loss: 1.709e-01 Loss in Target Net: 4.414e-02 |
2020-02-04 04:59:28 Iteration 800 Training Loss: 1.674e-01 Loss in Target Net: 4.306e-02 |
2020-02-04 05:02:34 Iteration 850 Training Loss: 1.676e-01 Loss in Target Net: 4.366e-02 |
2020-02-04 05:05:41 Iteration 900 Training Loss: 1.680e-01 Loss in Target Net: 4.337e-02 |
2020-02-04 05:08:47 Iteration 950 Training Loss: 1.672e-01 Loss in Target Net: 3.736e-02 |
2020-02-04 05:11:53 Iteration 1000 Training Loss: 1.654e-01 Loss in Target Net: 4.139e-02 |
2020-02-04 05:14:59 Iteration 1050 Training Loss: 1.671e-01 Loss in Target Net: 4.176e-02 |
2020-02-04 05:18:04 Iteration 1100 Training Loss: 1.663e-01 Loss in Target Net: 4.017e-02 |
2020-02-04 05:21:09 Iteration 1150 Training Loss: 1.650e-01 Loss in Target Net: 4.217e-02 |
2020-02-04 05:24:16 Iteration 1200 Training Loss: 1.632e-01 Loss in Target Net: 3.813e-02 |
2020-02-04 05:27:22 Iteration 1250 Training Loss: 1.663e-01 Loss in Target Net: 4.114e-02 |
2020-02-04 05:30:28 Iteration 1300 Training Loss: 1.655e-01 Loss in Target Net: 4.149e-02 |
2020-02-04 05:33:37 Iteration 1350 Training Loss: 1.650e-01 Loss in Target Net: 4.104e-02 |
2020-02-04 05:36:44 Iteration 1400 Training Loss: 1.647e-01 Loss in Target Net: 4.839e-02 |
2020-02-04 05:39:53 Iteration 1450 Training Loss: 1.655e-01 Loss in Target Net: 4.022e-02 |
2020-02-04 05:42:56 Iteration 1499 Training Loss: 1.640e-01 Loss in Target Net: 3.975e-02 |
Evaluating against victims networks |
DPN92 |
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