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2020-02-04 00:50:51 Iteration 300 Training Loss: 1.870e-01 Loss in Target Net: 4.729e-02 |
2020-02-04 00:54:03 Iteration 350 Training Loss: 1.880e-01 Loss in Target Net: 5.506e-02 |
2020-02-04 00:57:15 Iteration 400 Training Loss: 1.840e-01 Loss in Target Net: 6.218e-02 |
2020-02-04 01:00:25 Iteration 450 Training Loss: 1.832e-01 Loss in Target Net: 6.790e-02 |
2020-02-04 01:03:36 Iteration 500 Training Loss: 1.815e-01 Loss in Target Net: 7.959e-02 |
2020-02-04 01:06:47 Iteration 550 Training Loss: 1.824e-01 Loss in Target Net: 8.694e-02 |
2020-02-04 01:09:58 Iteration 600 Training Loss: 1.795e-01 Loss in Target Net: 8.791e-02 |
2020-02-04 01:13:09 Iteration 650 Training Loss: 1.754e-01 Loss in Target Net: 8.886e-02 |
2020-02-04 01:16:20 Iteration 700 Training Loss: 1.822e-01 Loss in Target Net: 8.054e-02 |
2020-02-04 01:19:31 Iteration 750 Training Loss: 1.801e-01 Loss in Target Net: 7.110e-02 |
2020-02-04 01:22:43 Iteration 800 Training Loss: 1.743e-01 Loss in Target Net: 7.196e-02 |
2020-02-04 01:25:53 Iteration 850 Training Loss: 1.761e-01 Loss in Target Net: 7.365e-02 |
2020-02-04 01:29:08 Iteration 900 Training Loss: 1.795e-01 Loss in Target Net: 7.702e-02 |
2020-02-04 01:32:24 Iteration 950 Training Loss: 1.767e-01 Loss in Target Net: 6.861e-02 |
2020-02-04 01:35:36 Iteration 1000 Training Loss: 1.756e-01 Loss in Target Net: 7.468e-02 |
2020-02-04 01:38:47 Iteration 1050 Training Loss: 1.726e-01 Loss in Target Net: 6.932e-02 |
2020-02-04 01:41:57 Iteration 1100 Training Loss: 1.717e-01 Loss in Target Net: 6.653e-02 |
2020-02-04 01:45:08 Iteration 1150 Training Loss: 1.746e-01 Loss in Target Net: 6.992e-02 |
2020-02-04 01:48:18 Iteration 1200 Training Loss: 1.697e-01 Loss in Target Net: 6.249e-02 |
2020-02-04 01:51:29 Iteration 1250 Training Loss: 1.695e-01 Loss in Target Net: 4.980e-02 |
2020-02-04 01:54:40 Iteration 1300 Training Loss: 1.705e-01 Loss in Target Net: 6.520e-02 |
2020-02-04 01:57:50 Iteration 1350 Training Loss: 1.706e-01 Loss in Target Net: 6.414e-02 |
2020-02-04 02:01:00 Iteration 1400 Training Loss: 1.696e-01 Loss in Target Net: 5.416e-02 |
2020-02-04 02:04:10 Iteration 1450 Training Loss: 1.659e-01 Loss in Target Net: 8.867e-02 |
2020-02-04 02:07:16 Iteration 1499 Training Loss: 1.684e-01 Loss in Target Net: 6.732e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to datasets/cifar-10-python.tar.gz |
Extracting datasets/cifar-10-python.tar.gz to datasets |
2020-02-04 02:08:15, Epoch 0, Iteration 7, loss 0.387 (0.467), acc 94.231 (91.600) |
2020-02-04 02:13:10, 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:[-1.9874855, -1.8192827, -0.32514, 1.9936634, 0.2884128, -3.228549, 9.0601635, -4.2760086, 3.260566, -2.748258], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:18:37 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-04 02:19:29 Epoch 59, Val iteration 19, acc 92.000 (92.590) |
* Prec: 92.59000129699707 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 95 |
TARGET INDEX: 0 |
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=1, 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/1500/1 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:32:02 Iteration 0 Training Loss: 1.026e+00 Loss in Target Net: 1.371e+00 |
2020-02-04 00:35:08 Iteration 50 Training Loss: 2.392e-01 Loss in Target Net: 4.448e-02 |
2020-02-04 00:38:20 Iteration 100 Training Loss: 2.134e-01 Loss in Target Net: 2.926e-02 |
2020-02-04 00:41:34 Iteration 150 Training Loss: 2.032e-01 Loss in Target Net: 2.684e-02 |
2020-02-04 00:44:47 Iteration 200 Training Loss: 1.965e-01 Loss in Target Net: 2.709e-02 |
2020-02-04 00:48:00 Iteration 250 Training Loss: 1.900e-01 Loss in Target Net: 3.085e-02 |
2020-02-04 00:51:13 Iteration 300 Training Loss: 1.877e-01 Loss in Target Net: 3.070e-02 |
2020-02-04 00:54:26 Iteration 350 Training Loss: 1.871e-01 Loss in Target Net: 3.009e-02 |
2020-02-04 00:57:42 Iteration 400 Training Loss: 1.844e-01 Loss in Target Net: 2.075e-02 |
2020-02-04 01:00:57 Iteration 450 Training Loss: 1.789e-01 Loss in Target Net: 2.187e-02 |
2020-02-04 01:04:12 Iteration 500 Training Loss: 1.820e-01 Loss in Target Net: 3.011e-02 |
2020-02-04 01:07:27 Iteration 550 Training Loss: 1.801e-01 Loss in Target Net: 2.906e-02 |
2020-02-04 01:10:41 Iteration 600 Training Loss: 1.771e-01 Loss in Target Net: 2.606e-02 |
2020-02-04 01:13:56 Iteration 650 Training Loss: 1.756e-01 Loss in Target Net: 2.335e-02 |
2020-02-04 01:17:10 Iteration 700 Training Loss: 1.771e-01 Loss in Target Net: 2.673e-02 |
2020-02-04 01:20:25 Iteration 750 Training Loss: 1.753e-01 Loss in Target Net: 3.083e-02 |
2020-02-04 01:23:41 Iteration 800 Training Loss: 1.756e-01 Loss in Target Net: 3.028e-02 |
2020-02-04 01:26:55 Iteration 850 Training Loss: 1.748e-01 Loss in Target Net: 2.768e-02 |
2020-02-04 01:30:10 Iteration 900 Training Loss: 1.777e-01 Loss in Target Net: 2.479e-02 |
2020-02-04 01:33:25 Iteration 950 Training Loss: 1.752e-01 Loss in Target Net: 3.072e-02 |
2020-02-04 01:36:40 Iteration 1000 Training Loss: 1.748e-01 Loss in Target Net: 2.640e-02 |
2020-02-04 01:39:56 Iteration 1050 Training Loss: 1.751e-01 Loss in Target Net: 3.922e-02 |
2020-02-04 01:43:11 Iteration 1100 Training Loss: 1.762e-01 Loss in Target Net: 3.230e-02 |
2020-02-04 01:46:28 Iteration 1150 Training Loss: 1.767e-01 Loss in Target Net: 3.153e-02 |
2020-02-04 01:49:45 Iteration 1200 Training Loss: 1.750e-01 Loss in Target Net: 3.180e-02 |
2020-02-04 01:52:59 Iteration 1250 Training Loss: 1.743e-01 Loss in Target Net: 3.516e-02 |
2020-02-04 01:56:13 Iteration 1300 Training Loss: 1.790e-01 Loss in Target Net: 2.581e-02 |
2020-02-04 01:59:29 Iteration 1350 Training Loss: 1.724e-01 Loss in Target Net: 3.400e-02 |
2020-02-04 02:02:46 Iteration 1400 Training Loss: 1.729e-01 Loss in Target Net: 3.592e-02 |
2020-02-04 02:06:02 Iteration 1450 Training Loss: 1.732e-01 Loss in Target Net: 3.048e-02 |
2020-02-04 02:09:10 Iteration 1499 Training Loss: 1.716e-01 Loss in Target Net: 5.963e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:10:04, Epoch 0, Iteration 7, loss 0.200 (0.530), acc 90.385 (89.000) |
2020-02-04 02:15:03, 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:[0.38477567, -0.5899315, -0.41593373, -2.2145076, -0.6949658, -5.207755, 4.0137, -2.4222164, 7.454526, 0.17143634], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:20:31 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 02:21:19 Epoch 59, Val iteration 19, acc 91.800 (92.700) |
* Prec: 92.70000114440919 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 1 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='10', 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=10, 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/1500/10 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:33:03 Iteration 0 Training Loss: 1.000e+00 Loss in Target Net: 1.287e+00 |
2020-02-04 00:36:32 Iteration 50 Training Loss: 1.943e-01 Loss in Target Net: 1.578e-02 |
2020-02-04 00:39:59 Iteration 100 Training Loss: 1.670e-01 Loss in Target Net: 1.675e-02 |
2020-02-04 00:43:27 Iteration 150 Training Loss: 1.553e-01 Loss in Target Net: 1.479e-02 |
2020-02-04 00:46:52 Iteration 200 Training Loss: 1.482e-01 Loss in Target Net: 1.225e-02 |
2020-02-04 00:50:20 Iteration 250 Training Loss: 1.487e-01 Loss in Target Net: 1.257e-02 |
2020-02-04 00:53:42 Iteration 300 Training Loss: 1.468e-01 Loss in Target Net: 1.236e-02 |
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