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2020-02-04 01:08:07 Iteration 550 Training Loss: 1.885e-01 Loss in Target Net: 1.887e-01 |
2020-02-04 01:11:16 Iteration 600 Training Loss: 1.830e-01 Loss in Target Net: 2.053e-01 |
2020-02-04 01:14:25 Iteration 650 Training Loss: 1.833e-01 Loss in Target Net: 1.849e-01 |
2020-02-04 01:17:34 Iteration 700 Training Loss: 1.858e-01 Loss in Target Net: 1.771e-01 |
2020-02-04 01:20:43 Iteration 750 Training Loss: 1.805e-01 Loss in Target Net: 1.536e-01 |
2020-02-04 01:23:52 Iteration 800 Training Loss: 1.789e-01 Loss in Target Net: 2.046e-01 |
2020-02-04 01:27:00 Iteration 850 Training Loss: 1.838e-01 Loss in Target Net: 1.799e-01 |
2020-02-04 01:30:09 Iteration 900 Training Loss: 1.813e-01 Loss in Target Net: 2.048e-01 |
2020-02-04 01:33:17 Iteration 950 Training Loss: 1.826e-01 Loss in Target Net: 2.262e-01 |
2020-02-04 01:36:26 Iteration 1000 Training Loss: 1.796e-01 Loss in Target Net: 2.326e-01 |
2020-02-04 01:39:35 Iteration 1050 Training Loss: 1.809e-01 Loss in Target Net: 1.898e-01 |
2020-02-04 01:42:45 Iteration 1100 Training Loss: 1.769e-01 Loss in Target Net: 2.193e-01 |
2020-02-04 01:45:54 Iteration 1150 Training Loss: 1.782e-01 Loss in Target Net: 2.273e-01 |
2020-02-04 01:49:03 Iteration 1200 Training Loss: 1.779e-01 Loss in Target Net: 2.054e-01 |
2020-02-04 01:52:12 Iteration 1250 Training Loss: 1.785e-01 Loss in Target Net: 2.228e-01 |
2020-02-04 01:55:20 Iteration 1300 Training Loss: 1.780e-01 Loss in Target Net: 2.247e-01 |
2020-02-04 01:58:30 Iteration 1350 Training Loss: 1.801e-01 Loss in Target Net: 1.682e-01 |
2020-02-04 02:01:39 Iteration 1400 Training Loss: 1.823e-01 Loss in Target Net: 2.182e-01 |
2020-02-04 02:04:50 Iteration 1450 Training Loss: 1.815e-01 Loss in Target Net: 2.055e-01 |
2020-02-04 02:07:54 Iteration 1499 Training Loss: 1.740e-01 Loss in Target Net: 1.992e-01 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:08:58, Epoch 0, Iteration 7, loss 0.458 (0.479), acc 88.462 (89.000) |
2020-02-04 02:13:51, 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:[-3.4353, -1.9114914, -2.2563899, 0.8277648, -0.956174, 2.0963535, 4.2208347, -2.0774171, 4.204841, -0.5833616], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:19:22 Epoch 59, Val iteration 0, acc 92.400 (92.400) |
2020-02-04 02:20:11 Epoch 59, Val iteration 19, acc 93.000 (93.060) |
* Prec: 93.06000137329102 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 94 |
TARGET INDEX: 14 |
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=15, 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/1500/15 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:52:12 Iteration 0 Training Loss: 1.029e+00 Loss in Target Net: 1.520e+00 |
2020-02-04 00:55:24 Iteration 50 Training Loss: 2.669e-01 Loss in Target Net: 1.062e-01 |
2020-02-04 00:58:35 Iteration 100 Training Loss: 2.327e-01 Loss in Target Net: 1.140e-01 |
2020-02-04 01:01:47 Iteration 150 Training Loss: 2.173e-01 Loss in Target Net: 1.014e-01 |
2020-02-04 01:04:59 Iteration 200 Training Loss: 2.045e-01 Loss in Target Net: 9.334e-02 |
2020-02-04 01:08:11 Iteration 250 Training Loss: 2.030e-01 Loss in Target Net: 8.100e-02 |
2020-02-04 01:11:23 Iteration 300 Training Loss: 2.001e-01 Loss in Target Net: 6.769e-02 |
2020-02-04 01:14:35 Iteration 350 Training Loss: 1.935e-01 Loss in Target Net: 7.637e-02 |
2020-02-04 01:17:47 Iteration 400 Training Loss: 1.914e-01 Loss in Target Net: 7.238e-02 |
2020-02-04 01:20:59 Iteration 450 Training Loss: 1.848e-01 Loss in Target Net: 6.469e-02 |
2020-02-04 01:24:10 Iteration 500 Training Loss: 1.892e-01 Loss in Target Net: 6.308e-02 |
2020-02-04 01:27:22 Iteration 550 Training Loss: 1.845e-01 Loss in Target Net: 6.906e-02 |
2020-02-04 01:30:33 Iteration 600 Training Loss: 1.853e-01 Loss in Target Net: 5.948e-02 |
2020-02-04 01:33:45 Iteration 650 Training Loss: 1.843e-01 Loss in Target Net: 6.280e-02 |
2020-02-04 01:36:57 Iteration 700 Training Loss: 1.835e-01 Loss in Target Net: 7.020e-02 |
2020-02-04 01:40:10 Iteration 750 Training Loss: 1.813e-01 Loss in Target Net: 7.035e-02 |
2020-02-04 01:43:22 Iteration 800 Training Loss: 1.868e-01 Loss in Target Net: 6.377e-02 |
2020-02-04 01:46:33 Iteration 850 Training Loss: 1.781e-01 Loss in Target Net: 7.421e-02 |
2020-02-04 01:49:45 Iteration 900 Training Loss: 1.831e-01 Loss in Target Net: 8.166e-02 |
2020-02-04 01:52:59 Iteration 950 Training Loss: 1.778e-01 Loss in Target Net: 7.151e-02 |
2020-02-04 01:56:12 Iteration 1000 Training Loss: 1.778e-01 Loss in Target Net: 5.950e-02 |
2020-02-04 01:59:23 Iteration 1050 Training Loss: 1.778e-01 Loss in Target Net: 6.876e-02 |
2020-02-04 02:02:37 Iteration 1100 Training Loss: 1.758e-01 Loss in Target Net: 6.714e-02 |
2020-02-04 02:05:50 Iteration 1150 Training Loss: 1.795e-01 Loss in Target Net: 7.300e-02 |
2020-02-04 02:08:58 Iteration 1200 Training Loss: 1.776e-01 Loss in Target Net: 5.901e-02 |
2020-02-04 02:12:22 Iteration 1250 Training Loss: 1.808e-01 Loss in Target Net: 6.805e-02 |
2020-02-04 02:15:46 Iteration 1300 Training Loss: 1.764e-01 Loss in Target Net: 6.216e-02 |
2020-02-04 02:19:16 Iteration 1350 Training Loss: 1.789e-01 Loss in Target Net: 7.491e-02 |
2020-02-04 02:22:28 Iteration 1400 Training Loss: 1.761e-01 Loss in Target Net: 6.252e-02 |
2020-02-04 02:25:51 Iteration 1450 Training Loss: 1.771e-01 Loss in Target Net: 6.006e-02 |
2020-02-04 02:28:55 Iteration 1499 Training Loss: 1.746e-01 Loss in Target Net: 7.043e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:29:57, Epoch 0, Iteration 7, loss 0.632 (0.420), acc 82.692 (90.400) |
2020-02-04 02:34:39, 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:[-1.7959511, -2.197053, 2.70921, 0.1410695, -2.1532447, -2.391868, 2.2340364, -2.6463134, 6.867481, -0.5408412], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:39:42 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-02-04 02:40:30 Epoch 59, Val iteration 19, acc 91.200 (92.340) |
* Prec: 92.34000167846679 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 15 |
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=16, 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/1500/16 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:20:38 Iteration 0 Training Loss: 1.077e+00 Loss in Target Net: 1.421e+00 |
2020-02-04 02:24:01 Iteration 50 Training Loss: 2.541e-01 Loss in Target Net: 5.459e-02 |
2020-02-04 02:27:20 Iteration 100 Training Loss: 2.176e-01 Loss in Target Net: 3.268e-02 |
2020-02-04 02:30:28 Iteration 150 Training Loss: 2.033e-01 Loss in Target Net: 2.736e-02 |
2020-02-04 02:33:44 Iteration 200 Training Loss: 1.949e-01 Loss in Target Net: 2.307e-02 |
2020-02-04 02:37:02 Iteration 250 Training Loss: 1.887e-01 Loss in Target Net: 2.432e-02 |
2020-02-04 02:40:18 Iteration 300 Training Loss: 1.857e-01 Loss in Target Net: 2.670e-02 |
2020-02-04 02:43:30 Iteration 350 Training Loss: 1.822e-01 Loss in Target Net: 2.301e-02 |
2020-02-04 02:46:42 Iteration 400 Training Loss: 1.802e-01 Loss in Target Net: 2.003e-02 |
2020-02-04 02:49:55 Iteration 450 Training Loss: 1.803e-01 Loss in Target Net: 2.289e-02 |
2020-02-04 02:53:06 Iteration 500 Training Loss: 1.804e-01 Loss in Target Net: 2.214e-02 |
2020-02-04 02:56:17 Iteration 550 Training Loss: 1.787e-01 Loss in Target Net: 2.148e-02 |
2020-02-04 02:59:28 Iteration 600 Training Loss: 1.794e-01 Loss in Target Net: 2.194e-02 |
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