text stringlengths 5 1.13k |
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-3.759, 0.87949824, -1.751664, -0.794323, -1.5004454, -2.0832455, 4.346558, -1.8293839, 7.5933113, -0.933681], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 06:19:37 Epoch 59, Val iteration 0, acc 91.200 (91.200) |
2020-02-04 06:20:26 Epoch 59, Val iteration 19, acc 92.400 (92.990) |
* Prec: 92.99000244140625 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 100 |
TARGET INDEX: 38 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='7', 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=39, 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/1500/39 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:23:39 Iteration 0 Training Loss: 1.072e+00 Loss in Target Net: 1.543e+00 |
2020-02-04 04:27:00 Iteration 50 Training Loss: 2.400e-01 Loss in Target Net: 7.411e-02 |
2020-02-04 04:30:23 Iteration 100 Training Loss: 2.088e-01 Loss in Target Net: 9.524e-02 |
2020-02-04 04:33:48 Iteration 150 Training Loss: 1.986e-01 Loss in Target Net: 7.836e-02 |
2020-02-04 04:37:13 Iteration 200 Training Loss: 1.887e-01 Loss in Target Net: 9.961e-02 |
2020-02-04 04:40:38 Iteration 250 Training Loss: 1.880e-01 Loss in Target Net: 7.391e-02 |
2020-02-04 04:44:03 Iteration 300 Training Loss: 1.843e-01 Loss in Target Net: 6.319e-02 |
2020-02-04 04:47:32 Iteration 350 Training Loss: 1.798e-01 Loss in Target Net: 5.617e-02 |
2020-02-04 04:50:58 Iteration 400 Training Loss: 1.777e-01 Loss in Target Net: 7.003e-02 |
2020-02-04 04:54:23 Iteration 450 Training Loss: 1.763e-01 Loss in Target Net: 8.032e-02 |
2020-02-04 04:57:47 Iteration 500 Training Loss: 1.812e-01 Loss in Target Net: 8.222e-02 |
2020-02-04 05:01:14 Iteration 550 Training Loss: 1.754e-01 Loss in Target Net: 7.681e-02 |
2020-02-04 05:04:38 Iteration 600 Training Loss: 1.756e-01 Loss in Target Net: 9.202e-02 |
2020-02-04 05:08:03 Iteration 650 Training Loss: 1.724e-01 Loss in Target Net: 8.196e-02 |
2020-02-04 05:11:27 Iteration 700 Training Loss: 1.734e-01 Loss in Target Net: 7.488e-02 |
2020-02-04 05:14:52 Iteration 750 Training Loss: 1.715e-01 Loss in Target Net: 1.157e-01 |
2020-02-04 05:18:15 Iteration 800 Training Loss: 1.694e-01 Loss in Target Net: 1.393e-01 |
2020-02-04 05:21:39 Iteration 850 Training Loss: 1.688e-01 Loss in Target Net: 1.119e-01 |
2020-02-04 05:25:05 Iteration 900 Training Loss: 1.675e-01 Loss in Target Net: 9.276e-02 |
2020-02-04 05:28:29 Iteration 950 Training Loss: 1.736e-01 Loss in Target Net: 9.414e-02 |
2020-02-04 05:31:53 Iteration 1000 Training Loss: 1.691e-01 Loss in Target Net: 7.651e-02 |
2020-02-04 05:35:18 Iteration 1050 Training Loss: 1.691e-01 Loss in Target Net: 7.676e-02 |
2020-02-04 05:38:42 Iteration 1100 Training Loss: 1.673e-01 Loss in Target Net: 9.466e-02 |
2020-02-04 05:42:08 Iteration 1150 Training Loss: 1.691e-01 Loss in Target Net: 1.223e-01 |
2020-02-04 05:45:29 Iteration 1200 Training Loss: 1.674e-01 Loss in Target Net: 8.043e-02 |
2020-02-04 05:49:01 Iteration 1250 Training Loss: 1.726e-01 Loss in Target Net: 9.554e-02 |
2020-02-04 05:52:42 Iteration 1300 Training Loss: 1.678e-01 Loss in Target Net: 8.447e-02 |
2020-02-04 05:56:13 Iteration 1350 Training Loss: 1.698e-01 Loss in Target Net: 8.377e-02 |
2020-02-04 05:59:23 Iteration 1400 Training Loss: 1.692e-01 Loss in Target Net: 9.979e-02 |
2020-02-04 06:02:25 Iteration 1450 Training Loss: 1.687e-01 Loss in Target Net: 9.051e-02 |
2020-02-04 06:05:19 Iteration 1499 Training Loss: 1.660e-01 Loss in Target Net: 9.592e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 06:06:16, Epoch 0, Iteration 7, loss 0.291 (0.413), acc 92.308 (90.000) |
2020-02-04 06:11:03, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:4, Target's Score:[-3.0590165, -1.9107919, -1.4052294, -0.44435397, 3.9835112, 0.6414235, 1.9659265, -1.845257, 3.1739745, -0.8467034], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 06:16:49 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-02-04 06:17:42 Epoch 59, Val iteration 19, acc 92.600 (92.780) |
* Prec: 92.78000106811524 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 102 |
TARGET INDEX: 39 |
DPN92 0 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='4', 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=4, 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/1500/4 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:32:19 Iteration 0 Training Loss: 1.009e+00 Loss in Target Net: 1.261e+00 |
2020-02-04 00:35:25 Iteration 50 Training Loss: 2.093e-01 Loss in Target Net: 7.903e-02 |
2020-02-04 00:38:36 Iteration 100 Training Loss: 1.800e-01 Loss in Target Net: 4.015e-02 |
2020-02-04 00:41:49 Iteration 150 Training Loss: 1.678e-01 Loss in Target Net: 3.177e-02 |
2020-02-04 00:45:01 Iteration 200 Training Loss: 1.594e-01 Loss in Target Net: 3.247e-02 |
2020-02-04 00:48:15 Iteration 250 Training Loss: 1.607e-01 Loss in Target Net: 2.825e-02 |
2020-02-04 00:51:24 Iteration 300 Training Loss: 1.544e-01 Loss in Target Net: 2.396e-02 |
2020-02-04 00:54:33 Iteration 350 Training Loss: 1.518e-01 Loss in Target Net: 2.694e-02 |
2020-02-04 00:57:44 Iteration 400 Training Loss: 1.555e-01 Loss in Target Net: 2.445e-02 |
2020-02-04 01:00:55 Iteration 450 Training Loss: 1.510e-01 Loss in Target Net: 2.276e-02 |
2020-02-04 01:04:07 Iteration 500 Training Loss: 1.480e-01 Loss in Target Net: 2.312e-02 |
2020-02-04 01:07:19 Iteration 550 Training Loss: 1.471e-01 Loss in Target Net: 2.303e-02 |
2020-02-04 01:10:29 Iteration 600 Training Loss: 1.484e-01 Loss in Target Net: 2.376e-02 |
2020-02-04 01:13:39 Iteration 650 Training Loss: 1.464e-01 Loss in Target Net: 2.278e-02 |
2020-02-04 01:16:49 Iteration 700 Training Loss: 1.489e-01 Loss in Target Net: 2.596e-02 |
2020-02-04 01:20:01 Iteration 750 Training Loss: 1.457e-01 Loss in Target Net: 2.387e-02 |
2020-02-04 01:23:13 Iteration 800 Training Loss: 1.485e-01 Loss in Target Net: 2.778e-02 |
2020-02-04 01:26:23 Iteration 850 Training Loss: 1.455e-01 Loss in Target Net: 2.695e-02 |
2020-02-04 01:29:33 Iteration 900 Training Loss: 1.468e-01 Loss in Target Net: 3.002e-02 |
2020-02-04 01:32:44 Iteration 950 Training Loss: 1.463e-01 Loss in Target Net: 2.505e-02 |
2020-02-04 01:35:54 Iteration 1000 Training Loss: 1.435e-01 Loss in Target Net: 2.551e-02 |
2020-02-04 01:39:04 Iteration 1050 Training Loss: 1.428e-01 Loss in Target Net: 2.838e-02 |
2020-02-04 01:42:16 Iteration 1100 Training Loss: 1.445e-01 Loss in Target Net: 2.606e-02 |
2020-02-04 01:45:27 Iteration 1150 Training Loss: 1.445e-01 Loss in Target Net: 2.868e-02 |
2020-02-04 01:48:37 Iteration 1200 Training Loss: 1.438e-01 Loss in Target Net: 2.512e-02 |
2020-02-04 01:51:47 Iteration 1250 Training Loss: 1.444e-01 Loss in Target Net: 2.450e-02 |
2020-02-04 01:54:58 Iteration 1300 Training Loss: 1.436e-01 Loss in Target Net: 2.740e-02 |
2020-02-04 01:58:10 Iteration 1350 Training Loss: 1.418e-01 Loss in Target Net: 2.701e-02 |
2020-02-04 02:01:21 Iteration 1400 Training Loss: 1.454e-01 Loss in Target Net: 2.443e-02 |
2020-02-04 02:04:31 Iteration 1450 Training Loss: 1.444e-01 Loss in Target Net: 2.588e-02 |
2020-02-04 02:07:38 Iteration 1499 Training Loss: 1.449e-01 Loss in Target Net: 2.935e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:08:40, Epoch 0, Iteration 7, loss 0.409 (0.524), acc 92.308 (88.600) |
2020-02-04 02:13:37, 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:[-0.6949839, 0.6214739, -2.5139577, -3.4572084, -1.7754174, -3.5684872, 6.211748, -2.433564, 10.526129, -2.5775087], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:19:12 Epoch 59, Val iteration 0, acc 94.800 (94.800) |
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