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2020-02-04 04:02:53 Iteration 1350 Training Loss: 1.800e-01 Loss in Target Net: 4.300e-02 |
2020-02-04 04:06:38 Iteration 1400 Training Loss: 1.805e-01 Loss in Target Net: 4.878e-02 |
2020-02-04 04:09:59 Iteration 1450 Training Loss: 1.789e-01 Loss in Target Net: 4.349e-02 |
2020-02-04 04:13:10 Iteration 1499 Training Loss: 1.786e-01 Loss in Target Net: 4.409e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:14:13, Epoch 0, Iteration 7, loss 0.753 (0.594), acc 76.923 (88.000) |
2020-02-04 04:19:34, 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:[-3.5067677, 0.23679805, -1.0336599, -0.6860838, -1.5657511, -3.1958282, 6.150443, -1.1070955, 7.4138656, -2.4082472], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:25:11 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-04 04:26:02 Epoch 59, Val iteration 19, acc 91.800 (92.590) |
* Prec: 92.59000167846679 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 103 |
TARGET INDEX: 29 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='3', 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=3, 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/1500/3 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:32:14 Iteration 0 Training Loss: 1.007e+00 Loss in Target Net: 1.319e+00 |
2020-02-04 00:35:22 Iteration 50 Training Loss: 2.113e-01 Loss in Target Net: 3.552e-02 |
2020-02-04 00:38:35 Iteration 100 Training Loss: 1.796e-01 Loss in Target Net: 3.178e-02 |
2020-02-04 00:41:49 Iteration 150 Training Loss: 1.686e-01 Loss in Target Net: 2.885e-02 |
2020-02-04 00:45:02 Iteration 200 Training Loss: 1.643e-01 Loss in Target Net: 2.893e-02 |
2020-02-04 00:48:14 Iteration 250 Training Loss: 1.580e-01 Loss in Target Net: 2.913e-02 |
2020-02-04 00:51:26 Iteration 300 Training Loss: 1.560e-01 Loss in Target Net: 2.736e-02 |
2020-02-04 00:54:38 Iteration 350 Training Loss: 1.536e-01 Loss in Target Net: 2.840e-02 |
2020-02-04 00:57:53 Iteration 400 Training Loss: 1.525e-01 Loss in Target Net: 2.709e-02 |
2020-02-04 01:01:10 Iteration 450 Training Loss: 1.520e-01 Loss in Target Net: 2.750e-02 |
2020-02-04 01:04:27 Iteration 500 Training Loss: 1.495e-01 Loss in Target Net: 2.728e-02 |
2020-02-04 01:07:44 Iteration 550 Training Loss: 1.477e-01 Loss in Target Net: 2.741e-02 |
2020-02-04 01:11:01 Iteration 600 Training Loss: 1.500e-01 Loss in Target Net: 2.895e-02 |
2020-02-04 01:14:17 Iteration 650 Training Loss: 1.510e-01 Loss in Target Net: 2.756e-02 |
2020-02-04 01:17:32 Iteration 700 Training Loss: 1.486e-01 Loss in Target Net: 2.629e-02 |
2020-02-04 01:20:45 Iteration 750 Training Loss: 1.499e-01 Loss in Target Net: 2.455e-02 |
2020-02-04 01:24:01 Iteration 800 Training Loss: 1.473e-01 Loss in Target Net: 2.767e-02 |
2020-02-04 01:27:15 Iteration 850 Training Loss: 1.478e-01 Loss in Target Net: 2.565e-02 |
2020-02-04 01:30:30 Iteration 900 Training Loss: 1.464e-01 Loss in Target Net: 2.840e-02 |
2020-02-04 01:33:48 Iteration 950 Training Loss: 1.486e-01 Loss in Target Net: 2.549e-02 |
2020-02-04 01:37:04 Iteration 1000 Training Loss: 1.482e-01 Loss in Target Net: 2.278e-02 |
2020-02-04 01:40:18 Iteration 1050 Training Loss: 1.474e-01 Loss in Target Net: 2.240e-02 |
2020-02-04 01:43:34 Iteration 1100 Training Loss: 1.481e-01 Loss in Target Net: 2.199e-02 |
2020-02-04 01:46:48 Iteration 1150 Training Loss: 1.459e-01 Loss in Target Net: 2.355e-02 |
2020-02-04 01:50:01 Iteration 1200 Training Loss: 1.462e-01 Loss in Target Net: 2.304e-02 |
2020-02-04 01:53:18 Iteration 1250 Training Loss: 1.460e-01 Loss in Target Net: 2.915e-02 |
2020-02-04 01:56:33 Iteration 1300 Training Loss: 1.445e-01 Loss in Target Net: 2.496e-02 |
2020-02-04 01:59:49 Iteration 1350 Training Loss: 1.460e-01 Loss in Target Net: 2.157e-02 |
2020-02-04 02:03:06 Iteration 1400 Training Loss: 1.466e-01 Loss in Target Net: 2.379e-02 |
2020-02-04 02:06:20 Iteration 1450 Training Loss: 1.432e-01 Loss in Target Net: 2.520e-02 |
2020-02-04 02:09:28 Iteration 1499 Training Loss: 1.468e-01 Loss in Target Net: 2.662e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:10:25, Epoch 0, Iteration 7, loss 0.575 (0.652), acc 86.538 (86.800) |
2020-02-04 02:15:30, 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:[-4.2757134, -1.1739688, -1.1273907, 0.6657501, -2.4619339, -1.138406, 8.871741, -2.6343858, 5.638496, -2.1514802], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:21:03 Epoch 59, Val iteration 0, acc 94.200 (94.200) |
2020-02-04 02:21:53 Epoch 59, Val iteration 19, acc 94.400 (93.720) |
* Prec: 93.72000236511231 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 3 |
DPN92 0 |
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=30, 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/1500/30 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:21:17 Iteration 0 Training Loss: 1.034e+00 Loss in Target Net: 1.397e+00 |
2020-02-04 02:24:37 Iteration 50 Training Loss: 2.201e-01 Loss in Target Net: 4.166e-02 |
2020-02-04 02:27:48 Iteration 100 Training Loss: 1.933e-01 Loss in Target Net: 3.452e-02 |
2020-02-04 02:30:46 Iteration 150 Training Loss: 1.763e-01 Loss in Target Net: 3.293e-02 |
2020-02-04 02:33:56 Iteration 200 Training Loss: 1.723e-01 Loss in Target Net: 3.213e-02 |
2020-02-04 02:37:06 Iteration 250 Training Loss: 1.664e-01 Loss in Target Net: 2.655e-02 |
2020-02-04 02:40:15 Iteration 300 Training Loss: 1.633e-01 Loss in Target Net: 2.654e-02 |
2020-02-04 02:43:24 Iteration 350 Training Loss: 1.609e-01 Loss in Target Net: 2.718e-02 |
2020-02-04 02:46:32 Iteration 400 Training Loss: 1.573e-01 Loss in Target Net: 2.835e-02 |
2020-02-04 02:49:39 Iteration 450 Training Loss: 1.580e-01 Loss in Target Net: 2.779e-02 |
2020-02-04 02:52:46 Iteration 500 Training Loss: 1.559e-01 Loss in Target Net: 2.738e-02 |
2020-02-04 02:55:54 Iteration 550 Training Loss: 1.565e-01 Loss in Target Net: 2.523e-02 |
2020-02-04 02:59:02 Iteration 600 Training Loss: 1.558e-01 Loss in Target Net: 2.226e-02 |
2020-02-04 03:02:10 Iteration 650 Training Loss: 1.549e-01 Loss in Target Net: 2.209e-02 |
2020-02-04 03:05:19 Iteration 700 Training Loss: 1.537e-01 Loss in Target Net: 2.089e-02 |
2020-02-04 03:08:27 Iteration 750 Training Loss: 1.540e-01 Loss in Target Net: 2.351e-02 |
2020-02-04 03:11:40 Iteration 800 Training Loss: 1.532e-01 Loss in Target Net: 2.251e-02 |
2020-02-04 03:14:50 Iteration 850 Training Loss: 1.529e-01 Loss in Target Net: 2.299e-02 |
2020-02-04 03:17:59 Iteration 900 Training Loss: 1.538e-01 Loss in Target Net: 2.406e-02 |
2020-02-04 03:21:11 Iteration 950 Training Loss: 1.512e-01 Loss in Target Net: 2.629e-02 |
2020-02-04 03:24:19 Iteration 1000 Training Loss: 1.514e-01 Loss in Target Net: 2.344e-02 |
2020-02-04 03:27:30 Iteration 1050 Training Loss: 1.538e-01 Loss in Target Net: 2.283e-02 |
2020-02-04 03:30:38 Iteration 1100 Training Loss: 1.516e-01 Loss in Target Net: 2.180e-02 |
2020-02-04 03:33:51 Iteration 1150 Training Loss: 1.524e-01 Loss in Target Net: 2.109e-02 |
2020-02-04 03:37:04 Iteration 1200 Training Loss: 1.510e-01 Loss in Target Net: 2.290e-02 |
2020-02-04 03:40:15 Iteration 1250 Training Loss: 1.519e-01 Loss in Target Net: 2.195e-02 |
2020-02-04 03:43:27 Iteration 1300 Training Loss: 1.529e-01 Loss in Target Net: 2.097e-02 |
2020-02-04 03:46:36 Iteration 1350 Training Loss: 1.515e-01 Loss in Target Net: 2.396e-02 |
2020-02-04 03:49:44 Iteration 1400 Training Loss: 1.499e-01 Loss in Target Net: 2.559e-02 |
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