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2020-02-04 03:51:52 Iteration 1250 Training Loss: 1.556e-01 Loss in Target Net: 3.828e-02 |
2020-02-04 03:55:12 Iteration 1300 Training Loss: 1.534e-01 Loss in Target Net: 3.761e-02 |
2020-02-04 03:58:30 Iteration 1350 Training Loss: 1.554e-01 Loss in Target Net: 3.494e-02 |
2020-02-04 04:02:00 Iteration 1400 Training Loss: 1.536e-01 Loss in Target Net: 3.227e-02 |
2020-02-04 04:05:44 Iteration 1450 Training Loss: 1.573e-01 Loss in Target Net: 3.315e-02 |
2020-02-04 04:09:11 Iteration 1499 Training Loss: 1.537e-01 Loss in Target Net: 3.690e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:10:48, Epoch 0, Iteration 7, loss 0.594 (0.499), acc 78.846 (89.600) |
2020-02-04 04:15:54, 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:[0.056268405, 2.8447487, -2.95874, -3.749009, -3.9452696, -4.5796604, 1.4319198, -2.0691717, 13.581438, -0.21889044], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:21:45 Epoch 59, Val iteration 0, acc 93.000 (93.000) |
2020-02-04 04:22:39 Epoch 59, Val iteration 19, acc 93.400 (93.100) |
* Prec: 93.10000152587891 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 101 |
TARGET INDEX: 27 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='12', 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=28, 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/1500/28 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:29:28 Iteration 0 Training Loss: 1.016e+00 Loss in Target Net: 1.331e+00 |
2020-02-04 02:32:55 Iteration 50 Training Loss: 1.965e-01 Loss in Target Net: 3.507e-02 |
2020-02-04 02:36:24 Iteration 100 Training Loss: 1.735e-01 Loss in Target Net: 2.883e-02 |
2020-02-04 02:39:51 Iteration 150 Training Loss: 1.589e-01 Loss in Target Net: 2.926e-02 |
2020-02-04 02:43:14 Iteration 200 Training Loss: 1.546e-01 Loss in Target Net: 2.237e-02 |
2020-02-04 02:46:40 Iteration 250 Training Loss: 1.522e-01 Loss in Target Net: 2.194e-02 |
2020-02-04 02:50:05 Iteration 300 Training Loss: 1.472e-01 Loss in Target Net: 2.137e-02 |
2020-02-04 02:53:29 Iteration 350 Training Loss: 1.474e-01 Loss in Target Net: 2.428e-02 |
2020-02-04 02:56:53 Iteration 400 Training Loss: 1.473e-01 Loss in Target Net: 2.337e-02 |
2020-02-04 03:00:19 Iteration 450 Training Loss: 1.452e-01 Loss in Target Net: 2.453e-02 |
2020-02-04 03:03:47 Iteration 500 Training Loss: 1.451e-01 Loss in Target Net: 2.279e-02 |
2020-02-04 03:07:13 Iteration 550 Training Loss: 1.421e-01 Loss in Target Net: 2.499e-02 |
2020-02-04 03:10:37 Iteration 600 Training Loss: 1.455e-01 Loss in Target Net: 2.284e-02 |
2020-02-04 03:14:04 Iteration 650 Training Loss: 1.433e-01 Loss in Target Net: 2.195e-02 |
2020-02-04 03:17:31 Iteration 700 Training Loss: 1.424e-01 Loss in Target Net: 2.420e-02 |
2020-02-04 03:20:57 Iteration 750 Training Loss: 1.406e-01 Loss in Target Net: 2.313e-02 |
2020-02-04 03:24:23 Iteration 800 Training Loss: 1.416e-01 Loss in Target Net: 2.414e-02 |
2020-02-04 03:27:52 Iteration 850 Training Loss: 1.402e-01 Loss in Target Net: 2.162e-02 |
2020-02-04 03:31:21 Iteration 900 Training Loss: 1.411e-01 Loss in Target Net: 2.311e-02 |
2020-02-04 03:34:47 Iteration 950 Training Loss: 1.397e-01 Loss in Target Net: 2.218e-02 |
2020-02-04 03:38:13 Iteration 1000 Training Loss: 1.404e-01 Loss in Target Net: 2.483e-02 |
2020-02-04 03:41:40 Iteration 1050 Training Loss: 1.385e-01 Loss in Target Net: 2.458e-02 |
2020-02-04 03:45:08 Iteration 1100 Training Loss: 1.430e-01 Loss in Target Net: 2.428e-02 |
2020-02-04 03:48:36 Iteration 1150 Training Loss: 1.391e-01 Loss in Target Net: 2.515e-02 |
2020-02-04 03:52:05 Iteration 1200 Training Loss: 1.402e-01 Loss in Target Net: 2.485e-02 |
2020-02-04 03:55:34 Iteration 1250 Training Loss: 1.389e-01 Loss in Target Net: 2.195e-02 |
2020-02-04 03:58:59 Iteration 1300 Training Loss: 1.389e-01 Loss in Target Net: 2.228e-02 |
2020-02-04 04:02:41 Iteration 1350 Training Loss: 1.402e-01 Loss in Target Net: 2.538e-02 |
2020-02-04 04:06:31 Iteration 1400 Training Loss: 1.384e-01 Loss in Target Net: 2.319e-02 |
2020-02-04 04:09:54 Iteration 1450 Training Loss: 1.400e-01 Loss in Target Net: 2.331e-02 |
2020-02-04 04:13:10 Iteration 1499 Training Loss: 1.420e-01 Loss in Target Net: 2.186e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:14:12, Epoch 0, Iteration 7, loss 0.415 (0.435), acc 92.308 (90.000) |
2020-02-04 04:19:34, 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:[-3.7641096, -0.43890837, 0.5878625, -0.1583648, -1.4271226, -3.5582392, 6.232489, -3.8929255, 9.542133, -2.836567], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:25:18 Epoch 59, Val iteration 0, acc 92.000 (92.000) |
2020-02-04 04:26:11 Epoch 59, Val iteration 19, acc 93.000 (93.030) |
* Prec: 93.03000106811524 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 104 |
TARGET INDEX: 28 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='13', 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=29, 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/1500/29 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:30:15 Iteration 0 Training Loss: 1.002e+00 Loss in Target Net: 1.334e+00 |
2020-02-04 02:33:44 Iteration 50 Training Loss: 2.596e-01 Loss in Target Net: 6.953e-02 |
2020-02-04 02:37:18 Iteration 100 Training Loss: 2.300e-01 Loss in Target Net: 5.616e-02 |
2020-02-04 02:40:43 Iteration 150 Training Loss: 2.137e-01 Loss in Target Net: 5.350e-02 |
2020-02-04 02:44:08 Iteration 200 Training Loss: 2.058e-01 Loss in Target Net: 5.597e-02 |
2020-02-04 02:47:33 Iteration 250 Training Loss: 1.993e-01 Loss in Target Net: 5.938e-02 |
2020-02-04 02:50:57 Iteration 300 Training Loss: 1.956e-01 Loss in Target Net: 4.844e-02 |
2020-02-04 02:54:23 Iteration 350 Training Loss: 1.930e-01 Loss in Target Net: 4.508e-02 |
2020-02-04 02:57:47 Iteration 400 Training Loss: 1.907e-01 Loss in Target Net: 4.633e-02 |
2020-02-04 03:01:13 Iteration 450 Training Loss: 1.870e-01 Loss in Target Net: 4.576e-02 |
2020-02-04 03:04:38 Iteration 500 Training Loss: 1.874e-01 Loss in Target Net: 4.700e-02 |
2020-02-04 03:08:04 Iteration 550 Training Loss: 1.879e-01 Loss in Target Net: 4.747e-02 |
2020-02-04 03:11:29 Iteration 600 Training Loss: 1.864e-01 Loss in Target Net: 5.098e-02 |
2020-02-04 03:14:54 Iteration 650 Training Loss: 1.829e-01 Loss in Target Net: 4.087e-02 |
2020-02-04 03:18:18 Iteration 700 Training Loss: 1.855e-01 Loss in Target Net: 4.552e-02 |
2020-02-04 03:21:42 Iteration 750 Training Loss: 1.827e-01 Loss in Target Net: 4.828e-02 |
2020-02-04 03:25:07 Iteration 800 Training Loss: 1.812e-01 Loss in Target Net: 4.595e-02 |
2020-02-04 03:28:31 Iteration 850 Training Loss: 1.820e-01 Loss in Target Net: 4.901e-02 |
2020-02-04 03:31:56 Iteration 900 Training Loss: 1.797e-01 Loss in Target Net: 5.086e-02 |
2020-02-04 03:35:21 Iteration 950 Training Loss: 1.811e-01 Loss in Target Net: 4.725e-02 |
2020-02-04 03:38:46 Iteration 1000 Training Loss: 1.787e-01 Loss in Target Net: 4.551e-02 |
2020-02-04 03:42:10 Iteration 1050 Training Loss: 1.823e-01 Loss in Target Net: 4.875e-02 |
2020-02-04 03:45:35 Iteration 1100 Training Loss: 1.801e-01 Loss in Target Net: 4.798e-02 |
2020-02-04 03:48:59 Iteration 1150 Training Loss: 1.806e-01 Loss in Target Net: 4.375e-02 |
2020-02-04 03:52:25 Iteration 1200 Training Loss: 1.786e-01 Loss in Target Net: 4.825e-02 |
2020-02-04 03:55:50 Iteration 1250 Training Loss: 1.803e-01 Loss in Target Net: 4.513e-02 |
2020-02-04 03:59:11 Iteration 1300 Training Loss: 1.792e-01 Loss in Target Net: 4.450e-02 |
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