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2020-02-04 00:57:09 Iteration 350 Training Loss: 1.413e-01 Loss in Target Net: 9.918e-03 |
2020-02-04 01:00:38 Iteration 400 Training Loss: 1.428e-01 Loss in Target Net: 1.100e-02 |
2020-02-04 01:04:08 Iteration 450 Training Loss: 1.396e-01 Loss in Target Net: 9.951e-03 |
2020-02-04 01:07:38 Iteration 500 Training Loss: 1.379e-01 Loss in Target Net: 9.524e-03 |
2020-02-04 01:11:08 Iteration 550 Training Loss: 1.421e-01 Loss in Target Net: 8.222e-03 |
2020-02-04 01:14:35 Iteration 600 Training Loss: 1.398e-01 Loss in Target Net: 9.116e-03 |
2020-02-04 01:18:05 Iteration 650 Training Loss: 1.390e-01 Loss in Target Net: 7.905e-03 |
2020-02-04 01:21:34 Iteration 700 Training Loss: 1.381e-01 Loss in Target Net: 9.588e-03 |
2020-02-04 01:25:03 Iteration 750 Training Loss: 1.374e-01 Loss in Target Net: 9.095e-03 |
2020-02-04 01:28:33 Iteration 800 Training Loss: 1.363e-01 Loss in Target Net: 9.108e-03 |
2020-02-04 01:32:01 Iteration 850 Training Loss: 1.373e-01 Loss in Target Net: 9.679e-03 |
2020-02-04 01:35:29 Iteration 900 Training Loss: 1.355e-01 Loss in Target Net: 1.045e-02 |
2020-02-04 01:38:56 Iteration 950 Training Loss: 1.371e-01 Loss in Target Net: 1.016e-02 |
2020-02-04 01:42:24 Iteration 1000 Training Loss: 1.384e-01 Loss in Target Net: 9.514e-03 |
2020-02-04 01:45:54 Iteration 1050 Training Loss: 1.385e-01 Loss in Target Net: 8.570e-03 |
2020-02-04 01:49:20 Iteration 1100 Training Loss: 1.367e-01 Loss in Target Net: 9.677e-03 |
2020-02-04 01:52:52 Iteration 1150 Training Loss: 1.368e-01 Loss in Target Net: 8.624e-03 |
2020-02-04 01:56:19 Iteration 1200 Training Loss: 1.361e-01 Loss in Target Net: 8.770e-03 |
2020-02-04 01:59:47 Iteration 1250 Training Loss: 1.376e-01 Loss in Target Net: 8.603e-03 |
2020-02-04 02:03:17 Iteration 1300 Training Loss: 1.377e-01 Loss in Target Net: 8.341e-03 |
2020-02-04 02:06:46 Iteration 1350 Training Loss: 1.372e-01 Loss in Target Net: 8.977e-03 |
2020-02-04 02:10:09 Iteration 1400 Training Loss: 1.366e-01 Loss in Target Net: 8.950e-03 |
2020-02-04 02:14:01 Iteration 1450 Training Loss: 1.346e-01 Loss in Target Net: 7.957e-03 |
2020-02-04 02:17:51 Iteration 1499 Training Loss: 1.352e-01 Loss in Target Net: 7.437e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:19:05, Epoch 0, Iteration 7, loss 0.381 (0.472), acc 86.538 (90.800) |
2020-02-04 02:24:17, 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:[-2.302877, -1.4271976, -1.1153233, -2.0153306, -1.1553541, -2.9411867, 6.356209, -1.452642, 7.1520824, -0.9744433], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:29:47 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-02-04 02:30:39 Epoch 59, Val iteration 19, acc 91.800 (92.860) |
* Prec: 92.86000137329101 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 105 |
TARGET INDEX: 10 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='11', 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=11, 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/1500/11 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:33:07 Iteration 0 Training Loss: 1.062e+00 Loss in Target Net: 1.587e+00 |
2020-02-04 00:36:26 Iteration 50 Training Loss: 2.779e-01 Loss in Target Net: 8.202e-02 |
2020-02-04 00:39:49 Iteration 100 Training Loss: 2.355e-01 Loss in Target Net: 5.926e-02 |
2020-02-04 00:43:08 Iteration 150 Training Loss: 2.193e-01 Loss in Target Net: 6.318e-02 |
2020-02-04 00:46:30 Iteration 200 Training Loss: 2.058e-01 Loss in Target Net: 4.544e-02 |
2020-02-04 00:49:52 Iteration 250 Training Loss: 2.032e-01 Loss in Target Net: 5.632e-02 |
2020-02-04 00:53:07 Iteration 300 Training Loss: 1.986e-01 Loss in Target Net: 5.439e-02 |
2020-02-04 00:56:29 Iteration 350 Training Loss: 1.967e-01 Loss in Target Net: 5.994e-02 |
2020-02-04 00:59:51 Iteration 400 Training Loss: 1.936e-01 Loss in Target Net: 6.013e-02 |
2020-02-04 01:03:12 Iteration 450 Training Loss: 1.900e-01 Loss in Target Net: 5.546e-02 |
2020-02-04 01:06:35 Iteration 500 Training Loss: 1.865e-01 Loss in Target Net: 6.104e-02 |
2020-02-04 01:09:57 Iteration 550 Training Loss: 1.897e-01 Loss in Target Net: 5.193e-02 |
2020-02-04 01:13:19 Iteration 600 Training Loss: 1.859e-01 Loss in Target Net: 5.782e-02 |
2020-02-04 01:16:41 Iteration 650 Training Loss: 1.882e-01 Loss in Target Net: 7.289e-02 |
2020-02-04 01:20:02 Iteration 700 Training Loss: 1.841e-01 Loss in Target Net: 7.615e-02 |
2020-02-04 01:23:27 Iteration 750 Training Loss: 1.815e-01 Loss in Target Net: 7.544e-02 |
2020-02-04 01:26:51 Iteration 800 Training Loss: 1.848e-01 Loss in Target Net: 8.509e-02 |
2020-02-04 01:30:13 Iteration 850 Training Loss: 1.795e-01 Loss in Target Net: 8.613e-02 |
2020-02-04 01:33:35 Iteration 900 Training Loss: 1.822e-01 Loss in Target Net: 6.617e-02 |
2020-02-04 01:36:57 Iteration 950 Training Loss: 1.809e-01 Loss in Target Net: 8.726e-02 |
2020-02-04 01:40:21 Iteration 1000 Training Loss: 1.803e-01 Loss in Target Net: 8.389e-02 |
2020-02-04 01:43:45 Iteration 1050 Training Loss: 1.788e-01 Loss in Target Net: 9.617e-02 |
2020-02-04 01:47:07 Iteration 1100 Training Loss: 1.781e-01 Loss in Target Net: 7.018e-02 |
2020-02-04 01:50:32 Iteration 1150 Training Loss: 1.787e-01 Loss in Target Net: 9.294e-02 |
2020-02-04 01:53:55 Iteration 1200 Training Loss: 1.815e-01 Loss in Target Net: 8.322e-02 |
2020-02-04 01:57:20 Iteration 1250 Training Loss: 1.762e-01 Loss in Target Net: 6.397e-02 |
2020-02-04 02:00:42 Iteration 1300 Training Loss: 1.761e-01 Loss in Target Net: 8.010e-02 |
2020-02-04 02:04:03 Iteration 1350 Training Loss: 1.752e-01 Loss in Target Net: 9.739e-02 |
2020-02-04 02:07:25 Iteration 1400 Training Loss: 1.793e-01 Loss in Target Net: 1.033e-01 |
2020-02-04 02:10:41 Iteration 1450 Training Loss: 1.755e-01 Loss in Target Net: 5.925e-02 |
2020-02-04 02:14:20 Iteration 1499 Training Loss: 1.761e-01 Loss in Target Net: 6.905e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:15:18, Epoch 0, Iteration 7, loss 0.834 (0.484), acc 75.000 (88.800) |
2020-02-04 02:20:38, 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:[-0.22059922, -0.5282209, -1.5991793, -2.6613448, -2.9690285, -2.6346765, 6.0166383, -0.8177591, 6.5743475, -1.1039556], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:26:16 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 02:27:06 Epoch 59, Val iteration 19, acc 93.000 (93.250) |
* Prec: 93.25000076293945 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 101 |
TARGET INDEX: 11 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='12', 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=12, 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/1500/12 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:33:34 Iteration 0 Training Loss: 9.916e-01 Loss in Target Net: 1.401e+00 |
2020-02-04 00:36:56 Iteration 50 Training Loss: 2.549e-01 Loss in Target Net: 1.786e-01 |
2020-02-04 00:40:17 Iteration 100 Training Loss: 2.197e-01 Loss in Target Net: 1.604e-01 |
2020-02-04 00:43:40 Iteration 150 Training Loss: 2.057e-01 Loss in Target Net: 1.198e-01 |
2020-02-04 00:47:01 Iteration 200 Training Loss: 2.006e-01 Loss in Target Net: 1.225e-01 |
2020-02-04 00:50:23 Iteration 250 Training Loss: 1.941e-01 Loss in Target Net: 1.031e-01 |
2020-02-04 00:53:43 Iteration 300 Training Loss: 1.953e-01 Loss in Target Net: 9.855e-02 |
2020-02-04 00:57:07 Iteration 350 Training Loss: 1.892e-01 Loss in Target Net: 1.034e-01 |
2020-02-04 01:00:29 Iteration 400 Training Loss: 1.902e-01 Loss in Target Net: 9.626e-02 |
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