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2020-02-04 03:02:39 Iteration 650 Training Loss: 1.785e-01 Loss in Target Net: 2.283e-02 |
2020-02-04 03:05:50 Iteration 700 Training Loss: 1.750e-01 Loss in Target Net: 2.164e-02 |
2020-02-04 03:09:02 Iteration 750 Training Loss: 1.726e-01 Loss in Target Net: 2.421e-02 |
2020-02-04 03:12:14 Iteration 800 Training Loss: 1.746e-01 Loss in Target Net: 2.334e-02 |
2020-02-04 03:15:25 Iteration 850 Training Loss: 1.739e-01 Loss in Target Net: 2.427e-02 |
2020-02-04 03:18:36 Iteration 900 Training Loss: 1.747e-01 Loss in Target Net: 2.602e-02 |
2020-02-04 03:21:46 Iteration 950 Training Loss: 1.738e-01 Loss in Target Net: 2.521e-02 |
2020-02-04 03:24:57 Iteration 1000 Training Loss: 1.727e-01 Loss in Target Net: 2.706e-02 |
2020-02-04 03:28:10 Iteration 1050 Training Loss: 1.723e-01 Loss in Target Net: 2.575e-02 |
2020-02-04 03:31:24 Iteration 1100 Training Loss: 1.719e-01 Loss in Target Net: 2.621e-02 |
2020-02-04 03:34:34 Iteration 1150 Training Loss: 1.724e-01 Loss in Target Net: 2.657e-02 |
2020-02-04 03:37:47 Iteration 1200 Training Loss: 1.724e-01 Loss in Target Net: 2.424e-02 |
2020-02-04 03:40:58 Iteration 1250 Training Loss: 1.712e-01 Loss in Target Net: 2.584e-02 |
2020-02-04 03:44:09 Iteration 1300 Training Loss: 1.724e-01 Loss in Target Net: 2.732e-02 |
2020-02-04 03:47:19 Iteration 1350 Training Loss: 1.710e-01 Loss in Target Net: 2.661e-02 |
2020-02-04 03:50:31 Iteration 1400 Training Loss: 1.711e-01 Loss in Target Net: 2.710e-02 |
2020-02-04 03:53:40 Iteration 1450 Training Loss: 1.721e-01 Loss in Target Net: 3.096e-02 |
2020-02-04 03:56:48 Iteration 1499 Training Loss: 1.728e-01 Loss in Target Net: 2.802e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:57:44, Epoch 0, Iteration 7, loss 0.274 (0.500), acc 96.154 (89.400) |
2020-02-04 04:02:38, 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:[-2.8727574, -0.41492853, -1.5891995, -0.73071676, -1.3969791, -1.4359463, 9.145593, -2.9165251, 4.584445, -2.0099554], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:08:07 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-02-04 04:08:58 Epoch 59, Val iteration 19, acc 91.800 (92.690) |
* Prec: 92.69000129699707 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 16 |
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=17, 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/1500/17 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:22:07 Iteration 0 Training Loss: 1.005e+00 Loss in Target Net: 1.346e+00 |
2020-02-04 02:25:34 Iteration 50 Training Loss: 2.121e-01 Loss in Target Net: 4.144e-02 |
2020-02-04 02:28:42 Iteration 100 Training Loss: 1.838e-01 Loss in Target Net: 4.180e-02 |
2020-02-04 02:31:49 Iteration 150 Training Loss: 1.748e-01 Loss in Target Net: 4.479e-02 |
2020-02-04 02:35:04 Iteration 200 Training Loss: 1.683e-01 Loss in Target Net: 5.212e-02 |
2020-02-04 02:38:18 Iteration 250 Training Loss: 1.638e-01 Loss in Target Net: 4.342e-02 |
2020-02-04 02:41:29 Iteration 300 Training Loss: 1.608e-01 Loss in Target Net: 4.228e-02 |
2020-02-04 02:44:42 Iteration 350 Training Loss: 1.598e-01 Loss in Target Net: 4.459e-02 |
2020-02-04 02:47:56 Iteration 400 Training Loss: 1.594e-01 Loss in Target Net: 4.276e-02 |
2020-02-04 02:51:10 Iteration 450 Training Loss: 1.574e-01 Loss in Target Net: 4.424e-02 |
2020-02-04 02:54:23 Iteration 500 Training Loss: 1.545e-01 Loss in Target Net: 4.510e-02 |
2020-02-04 02:57:35 Iteration 550 Training Loss: 1.592e-01 Loss in Target Net: 4.318e-02 |
2020-02-04 03:00:48 Iteration 600 Training Loss: 1.568e-01 Loss in Target Net: 4.535e-02 |
2020-02-04 03:04:03 Iteration 650 Training Loss: 1.536e-01 Loss in Target Net: 4.543e-02 |
2020-02-04 03:07:15 Iteration 700 Training Loss: 1.540e-01 Loss in Target Net: 4.580e-02 |
2020-02-04 03:10:31 Iteration 750 Training Loss: 1.530e-01 Loss in Target Net: 4.148e-02 |
2020-02-04 03:13:44 Iteration 800 Training Loss: 1.534e-01 Loss in Target Net: 3.878e-02 |
2020-02-04 03:16:54 Iteration 850 Training Loss: 1.542e-01 Loss in Target Net: 3.750e-02 |
2020-02-04 03:20:07 Iteration 900 Training Loss: 1.547e-01 Loss in Target Net: 4.243e-02 |
2020-02-04 03:23:19 Iteration 950 Training Loss: 1.543e-01 Loss in Target Net: 3.883e-02 |
2020-02-04 03:26:33 Iteration 1000 Training Loss: 1.524e-01 Loss in Target Net: 3.783e-02 |
2020-02-04 03:29:46 Iteration 1050 Training Loss: 1.559e-01 Loss in Target Net: 4.204e-02 |
2020-02-04 03:32:59 Iteration 1100 Training Loss: 1.540e-01 Loss in Target Net: 4.024e-02 |
2020-02-04 03:36:11 Iteration 1150 Training Loss: 1.512e-01 Loss in Target Net: 4.093e-02 |
2020-02-04 03:39:23 Iteration 1200 Training Loss: 1.536e-01 Loss in Target Net: 4.444e-02 |
2020-02-04 03:42:37 Iteration 1250 Training Loss: 1.540e-01 Loss in Target Net: 4.589e-02 |
2020-02-04 03:45:49 Iteration 1300 Training Loss: 1.535e-01 Loss in Target Net: 3.931e-02 |
2020-02-04 03:49:02 Iteration 1350 Training Loss: 1.531e-01 Loss in Target Net: 3.883e-02 |
2020-02-04 03:52:14 Iteration 1400 Training Loss: 1.525e-01 Loss in Target Net: 4.002e-02 |
2020-02-04 03:55:26 Iteration 1450 Training Loss: 1.515e-01 Loss in Target Net: 4.135e-02 |
2020-02-04 03:58:32 Iteration 1499 Training Loss: 1.525e-01 Loss in Target Net: 4.309e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:59:39, Epoch 0, Iteration 7, loss 0.612 (0.505), acc 84.615 (88.800) |
2020-02-04 04:04:41, 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:[-3.2584293, 0.23826355, -1.7680246, 1.7556798, -1.2443132, -2.1390426, 3.9996552, -2.4795291, 7.252619, -2.1435814], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:10:16 Epoch 59, Val iteration 0, acc 91.600 (91.600) |
2020-02-04 04:11:05 Epoch 59, Val iteration 19, acc 91.600 (92.720) |
* Prec: 92.72000083923339 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 96 |
TARGET INDEX: 17 |
DPN92 1 |
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=18, 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/1500/18 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:21:43 Iteration 0 Training Loss: 1.049e+00 Loss in Target Net: 1.407e+00 |
2020-02-04 02:25:04 Iteration 50 Training Loss: 2.492e-01 Loss in Target Net: 1.145e-01 |
2020-02-04 02:28:12 Iteration 100 Training Loss: 2.103e-01 Loss in Target Net: 5.284e-02 |
2020-02-04 02:31:13 Iteration 150 Training Loss: 1.898e-01 Loss in Target Net: 3.566e-02 |
2020-02-04 02:34:27 Iteration 200 Training Loss: 1.821e-01 Loss in Target Net: 3.215e-02 |
2020-02-04 02:37:38 Iteration 250 Training Loss: 1.759e-01 Loss in Target Net: 3.142e-02 |
2020-02-04 02:40:48 Iteration 300 Training Loss: 1.717e-01 Loss in Target Net: 3.277e-02 |
2020-02-04 02:43:56 Iteration 350 Training Loss: 1.706e-01 Loss in Target Net: 3.161e-02 |
2020-02-04 02:47:07 Iteration 400 Training Loss: 1.687e-01 Loss in Target Net: 3.469e-02 |
2020-02-04 02:50:20 Iteration 450 Training Loss: 1.666e-01 Loss in Target Net: 3.334e-02 |
2020-02-04 02:53:30 Iteration 500 Training Loss: 1.634e-01 Loss in Target Net: 4.130e-02 |
2020-02-04 02:56:38 Iteration 550 Training Loss: 1.607e-01 Loss in Target Net: 3.882e-02 |
2020-02-04 02:59:51 Iteration 600 Training Loss: 1.647e-01 Loss in Target Net: 4.165e-02 |
2020-02-04 03:03:02 Iteration 650 Training Loss: 1.648e-01 Loss in Target Net: 4.592e-02 |
2020-02-04 03:06:09 Iteration 700 Training Loss: 1.601e-01 Loss in Target Net: 3.976e-02 |
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