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2020-02-04 01:03:53 Iteration 450 Training Loss: 1.857e-01 Loss in Target Net: 7.886e-02 |
2020-02-04 01:07:16 Iteration 500 Training Loss: 1.843e-01 Loss in Target Net: 9.303e-02 |
2020-02-04 01:10:39 Iteration 550 Training Loss: 1.833e-01 Loss in Target Net: 8.366e-02 |
2020-02-04 01:14:01 Iteration 600 Training Loss: 1.878e-01 Loss in Target Net: 8.195e-02 |
2020-02-04 01:17:27 Iteration 650 Training Loss: 1.844e-01 Loss in Target Net: 8.284e-02 |
2020-02-04 01:20:52 Iteration 700 Training Loss: 1.805e-01 Loss in Target Net: 7.665e-02 |
2020-02-04 01:24:15 Iteration 750 Training Loss: 1.813e-01 Loss in Target Net: 6.466e-02 |
2020-02-04 01:27:41 Iteration 800 Training Loss: 1.803e-01 Loss in Target Net: 6.755e-02 |
2020-02-04 01:31:05 Iteration 850 Training Loss: 1.770e-01 Loss in Target Net: 7.892e-02 |
2020-02-04 01:34:28 Iteration 900 Training Loss: 1.798e-01 Loss in Target Net: 6.738e-02 |
2020-02-04 01:37:50 Iteration 950 Training Loss: 1.768e-01 Loss in Target Net: 8.803e-02 |
2020-02-04 01:41:14 Iteration 1000 Training Loss: 1.775e-01 Loss in Target Net: 6.677e-02 |
2020-02-04 01:44:37 Iteration 1050 Training Loss: 1.790e-01 Loss in Target Net: 7.937e-02 |
2020-02-04 01:47:59 Iteration 1100 Training Loss: 1.743e-01 Loss in Target Net: 8.292e-02 |
2020-02-04 01:51:23 Iteration 1150 Training Loss: 1.752e-01 Loss in Target Net: 7.082e-02 |
2020-02-04 01:54:47 Iteration 1200 Training Loss: 1.781e-01 Loss in Target Net: 7.365e-02 |
2020-02-04 01:58:10 Iteration 1250 Training Loss: 1.745e-01 Loss in Target Net: 7.686e-02 |
2020-02-04 02:01:34 Iteration 1300 Training Loss: 1.753e-01 Loss in Target Net: 7.443e-02 |
2020-02-04 02:04:58 Iteration 1350 Training Loss: 1.746e-01 Loss in Target Net: 7.327e-02 |
2020-02-04 02:08:18 Iteration 1400 Training Loss: 1.709e-01 Loss in Target Net: 6.435e-02 |
2020-02-04 02:11:49 Iteration 1450 Training Loss: 1.773e-01 Loss in Target Net: 7.317e-02 |
2020-02-04 02:15:29 Iteration 1499 Training Loss: 1.744e-01 Loss in Target Net: 5.755e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:16:38, Epoch 0, Iteration 7, loss 0.339 (0.481), acc 88.462 (91.000) |
2020-02-04 02:22:00, 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:[-2.852805, 0.001893891, -2.442598, -2.886468, -2.8315284, 1.128524, 5.8456283, -2.95514, 9.506295, -2.003997], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:27:44 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-04 02:28:36 Epoch 59, Val iteration 19, acc 93.600 (93.070) |
* Prec: 93.0700023651123 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 102 |
TARGET INDEX: 12 |
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=13, 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/1500/13 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:33:14 Iteration 0 Training Loss: 1.043e+00 Loss in Target Net: 1.444e+00 |
2020-02-04 00:36:37 Iteration 50 Training Loss: 2.190e-01 Loss in Target Net: 2.954e-02 |
2020-02-04 00:40:06 Iteration 100 Training Loss: 1.865e-01 Loss in Target Net: 2.340e-02 |
2020-02-04 00:43:29 Iteration 150 Training Loss: 1.723e-01 Loss in Target Net: 2.433e-02 |
2020-02-04 00:46:52 Iteration 200 Training Loss: 1.654e-01 Loss in Target Net: 2.606e-02 |
2020-02-04 00:50:13 Iteration 250 Training Loss: 1.631e-01 Loss in Target Net: 2.410e-02 |
2020-02-04 00:53:34 Iteration 300 Training Loss: 1.621e-01 Loss in Target Net: 2.118e-02 |
2020-02-04 00:56:57 Iteration 350 Training Loss: 1.580e-01 Loss in Target Net: 1.907e-02 |
2020-02-04 01:00:22 Iteration 400 Training Loss: 1.537e-01 Loss in Target Net: 2.207e-02 |
2020-02-04 01:03:46 Iteration 450 Training Loss: 1.533e-01 Loss in Target Net: 2.076e-02 |
2020-02-04 01:07:13 Iteration 500 Training Loss: 1.521e-01 Loss in Target Net: 1.771e-02 |
2020-02-04 01:10:37 Iteration 550 Training Loss: 1.528e-01 Loss in Target Net: 1.944e-02 |
2020-02-04 01:14:02 Iteration 600 Training Loss: 1.544e-01 Loss in Target Net: 1.805e-02 |
2020-02-04 01:17:29 Iteration 650 Training Loss: 1.509e-01 Loss in Target Net: 1.936e-02 |
2020-02-04 01:20:54 Iteration 700 Training Loss: 1.474e-01 Loss in Target Net: 1.836e-02 |
2020-02-04 01:24:19 Iteration 750 Training Loss: 1.502e-01 Loss in Target Net: 1.891e-02 |
2020-02-04 01:27:45 Iteration 800 Training Loss: 1.498e-01 Loss in Target Net: 1.867e-02 |
2020-02-04 01:31:09 Iteration 850 Training Loss: 1.514e-01 Loss in Target Net: 1.817e-02 |
2020-02-04 01:34:35 Iteration 900 Training Loss: 1.500e-01 Loss in Target Net: 1.886e-02 |
2020-02-04 01:38:01 Iteration 950 Training Loss: 1.481e-01 Loss in Target Net: 1.795e-02 |
2020-02-04 01:41:24 Iteration 1000 Training Loss: 1.477e-01 Loss in Target Net: 2.077e-02 |
2020-02-04 01:44:54 Iteration 1050 Training Loss: 1.477e-01 Loss in Target Net: 2.003e-02 |
2020-02-04 01:48:20 Iteration 1100 Training Loss: 1.492e-01 Loss in Target Net: 1.935e-02 |
2020-02-04 01:51:44 Iteration 1150 Training Loss: 1.466e-01 Loss in Target Net: 1.771e-02 |
2020-02-04 01:55:12 Iteration 1200 Training Loss: 1.467e-01 Loss in Target Net: 1.741e-02 |
2020-02-04 01:58:41 Iteration 1250 Training Loss: 1.507e-01 Loss in Target Net: 1.713e-02 |
2020-02-04 02:02:07 Iteration 1300 Training Loss: 1.461e-01 Loss in Target Net: 1.877e-02 |
2020-02-04 02:05:33 Iteration 1350 Training Loss: 1.482e-01 Loss in Target Net: 1.913e-02 |
2020-02-04 02:08:56 Iteration 1400 Training Loss: 1.479e-01 Loss in Target Net: 1.918e-02 |
2020-02-04 02:12:39 Iteration 1450 Training Loss: 1.467e-01 Loss in Target Net: 1.856e-02 |
2020-02-04 02:16:22 Iteration 1499 Training Loss: 1.484e-01 Loss in Target Net: 1.763e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:17:27, Epoch 0, Iteration 7, loss 0.289 (0.532), acc 90.385 (88.800) |
2020-02-04 02:22:42, 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:[-1.0557663, -0.8902325, -2.4739764, 0.8264859, -2.6277084, -2.8142767, 6.185136, -3.66907, 7.677969, -0.83239824], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:28:30 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-02-04 02:29:19 Epoch 59, Val iteration 19, acc 92.800 (92.740) |
* Prec: 92.74000129699706 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 103 |
TARGET INDEX: 13 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='14', 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=14, 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/1500/14 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:33:39 Iteration 0 Training Loss: 1.080e+00 Loss in Target Net: 1.722e+00 |
2020-02-04 00:36:47 Iteration 50 Training Loss: 2.578e-01 Loss in Target Net: 2.560e-01 |
2020-02-04 00:39:55 Iteration 100 Training Loss: 2.299e-01 Loss in Target Net: 2.493e-01 |
2020-02-04 00:43:02 Iteration 150 Training Loss: 2.143e-01 Loss in Target Net: 2.058e-01 |
2020-02-04 00:46:11 Iteration 200 Training Loss: 2.036e-01 Loss in Target Net: 2.078e-01 |
2020-02-04 00:49:19 Iteration 250 Training Loss: 1.957e-01 Loss in Target Net: 2.318e-01 |
2020-02-04 00:52:23 Iteration 300 Training Loss: 2.025e-01 Loss in Target Net: 2.234e-01 |
2020-02-04 00:55:32 Iteration 350 Training Loss: 2.019e-01 Loss in Target Net: 2.503e-01 |
2020-02-04 00:58:40 Iteration 400 Training Loss: 1.940e-01 Loss in Target Net: 2.228e-01 |
2020-02-04 01:01:49 Iteration 450 Training Loss: 1.884e-01 Loss in Target Net: 1.927e-01 |
2020-02-04 01:04:58 Iteration 500 Training Loss: 1.832e-01 Loss in Target Net: 1.877e-01 |
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