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2020-02-04 03:09:18 Iteration 750 Training Loss: 1.600e-01 Loss in Target Net: 5.065e-02 |
2020-02-04 03:12:26 Iteration 800 Training Loss: 1.594e-01 Loss in Target Net: 4.713e-02 |
2020-02-04 03:15:37 Iteration 850 Training Loss: 1.622e-01 Loss in Target Net: 3.631e-02 |
2020-02-04 03:18:44 Iteration 900 Training Loss: 1.616e-01 Loss in Target Net: 3.866e-02 |
2020-02-04 03:21:56 Iteration 950 Training Loss: 1.590e-01 Loss in Target Net: 4.680e-02 |
2020-02-04 03:25:06 Iteration 1000 Training Loss: 1.568e-01 Loss in Target Net: 5.415e-02 |
2020-02-04 03:28:15 Iteration 1050 Training Loss: 1.590e-01 Loss in Target Net: 4.459e-02 |
2020-02-04 03:31:26 Iteration 1100 Training Loss: 1.586e-01 Loss in Target Net: 4.737e-02 |
2020-02-04 03:34:36 Iteration 1150 Training Loss: 1.582e-01 Loss in Target Net: 4.579e-02 |
2020-02-04 03:37:44 Iteration 1200 Training Loss: 1.577e-01 Loss in Target Net: 3.762e-02 |
2020-02-04 03:40:56 Iteration 1250 Training Loss: 1.571e-01 Loss in Target Net: 3.821e-02 |
2020-02-04 03:44:04 Iteration 1300 Training Loss: 1.587e-01 Loss in Target Net: 4.588e-02 |
2020-02-04 03:47:12 Iteration 1350 Training Loss: 1.555e-01 Loss in Target Net: 4.546e-02 |
2020-02-04 03:50:21 Iteration 1400 Training Loss: 1.565e-01 Loss in Target Net: 4.123e-02 |
2020-02-04 03:53:31 Iteration 1450 Training Loss: 1.557e-01 Loss in Target Net: 3.928e-02 |
2020-02-04 03:56:39 Iteration 1499 Training Loss: 1.585e-01 Loss in Target Net: 3.659e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:57:37, Epoch 0, Iteration 7, loss 0.218 (0.393), acc 92.308 (92.000) |
2020-02-04 04:02:31, 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:[2.0232928, 0.41104114, -2.6573095, -1.4358954, -4.2424803, -4.903819, 3.5939562, -2.1053998, 9.7733, 0.18743813], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:07:55 Epoch 59, Val iteration 0, acc 93.000 (93.000) |
2020-02-04 04:08:45 Epoch 59, Val iteration 19, acc 92.600 (92.700) |
* Prec: 92.7000015258789 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 95 |
TARGET INDEX: 18 |
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=19, 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/1500/19 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:22:40 Iteration 0 Training Loss: 1.059e+00 Loss in Target Net: 1.636e+00 |
2020-02-04 02:26:03 Iteration 50 Training Loss: 2.589e-01 Loss in Target Net: 2.112e-01 |
2020-02-04 02:29:09 Iteration 100 Training Loss: 2.276e-01 Loss in Target Net: 2.178e-01 |
2020-02-04 02:32:18 Iteration 150 Training Loss: 2.113e-01 Loss in Target Net: 1.694e-01 |
2020-02-04 02:35:30 Iteration 200 Training Loss: 2.014e-01 Loss in Target Net: 1.795e-01 |
2020-02-04 02:38:43 Iteration 250 Training Loss: 1.997e-01 Loss in Target Net: 1.777e-01 |
2020-02-04 02:41:53 Iteration 300 Training Loss: 1.960e-01 Loss in Target Net: 1.801e-01 |
2020-02-04 02:45:03 Iteration 350 Training Loss: 1.941e-01 Loss in Target Net: 1.744e-01 |
2020-02-04 02:48:15 Iteration 400 Training Loss: 1.903e-01 Loss in Target Net: 1.606e-01 |
2020-02-04 02:51:26 Iteration 450 Training Loss: 1.877e-01 Loss in Target Net: 1.190e-01 |
2020-02-04 02:54:36 Iteration 500 Training Loss: 1.854e-01 Loss in Target Net: 1.335e-01 |
2020-02-04 02:57:47 Iteration 550 Training Loss: 1.819e-01 Loss in Target Net: 1.117e-01 |
2020-02-04 03:00:58 Iteration 600 Training Loss: 1.852e-01 Loss in Target Net: 1.567e-01 |
2020-02-04 03:04:09 Iteration 650 Training Loss: 1.861e-01 Loss in Target Net: 1.213e-01 |
2020-02-04 03:07:20 Iteration 700 Training Loss: 1.827e-01 Loss in Target Net: 1.273e-01 |
2020-02-04 03:10:31 Iteration 750 Training Loss: 1.833e-01 Loss in Target Net: 1.284e-01 |
2020-02-04 03:13:43 Iteration 800 Training Loss: 1.846e-01 Loss in Target Net: 1.402e-01 |
2020-02-04 03:16:54 Iteration 850 Training Loss: 1.809e-01 Loss in Target Net: 1.290e-01 |
2020-02-04 03:20:05 Iteration 900 Training Loss: 1.814e-01 Loss in Target Net: 1.162e-01 |
2020-02-04 03:23:16 Iteration 950 Training Loss: 1.826e-01 Loss in Target Net: 1.001e-01 |
2020-02-04 03:26:28 Iteration 1000 Training Loss: 1.809e-01 Loss in Target Net: 1.190e-01 |
2020-02-04 03:29:39 Iteration 1050 Training Loss: 1.802e-01 Loss in Target Net: 1.102e-01 |
2020-02-04 03:32:51 Iteration 1100 Training Loss: 1.793e-01 Loss in Target Net: 1.271e-01 |
2020-02-04 03:36:02 Iteration 1150 Training Loss: 1.795e-01 Loss in Target Net: 1.284e-01 |
2020-02-04 03:39:14 Iteration 1200 Training Loss: 1.774e-01 Loss in Target Net: 1.220e-01 |
2020-02-04 03:42:25 Iteration 1250 Training Loss: 1.813e-01 Loss in Target Net: 1.055e-01 |
2020-02-04 03:45:37 Iteration 1300 Training Loss: 1.793e-01 Loss in Target Net: 1.064e-01 |
2020-02-04 03:48:48 Iteration 1350 Training Loss: 1.783e-01 Loss in Target Net: 1.225e-01 |
2020-02-04 03:52:00 Iteration 1400 Training Loss: 1.740e-01 Loss in Target Net: 1.235e-01 |
2020-02-04 03:55:11 Iteration 1450 Training Loss: 1.781e-01 Loss in Target Net: 1.137e-01 |
2020-02-04 03:58:17 Iteration 1499 Training Loss: 1.725e-01 Loss in Target Net: 1.180e-01 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:59:21, Epoch 0, Iteration 7, loss 0.521 (0.521), acc 88.462 (87.400) |
2020-02-04 04:04:25, 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:[0.6858745, -0.7747357, -0.8407977, -2.7314801, -0.12398285, -2.7140322, 4.0642066, -1.8747772, 6.759412, -2.215547], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:10:01 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-04 04:10:50 Epoch 59, Val iteration 19, acc 93.800 (93.470) |
* Prec: 93.47000083923339 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 96 |
TARGET INDEX: 19 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='2', 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=2, 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/1500/2 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 00:32:11 Iteration 0 Training Loss: 1.000e+00 Loss in Target Net: 1.417e+00 |
2020-02-04 00:35:15 Iteration 50 Training Loss: 2.156e-01 Loss in Target Net: 5.140e-02 |
2020-02-04 00:38:29 Iteration 100 Training Loss: 1.919e-01 Loss in Target Net: 2.621e-02 |
2020-02-04 00:41:44 Iteration 150 Training Loss: 1.750e-01 Loss in Target Net: 2.463e-02 |
2020-02-04 00:45:00 Iteration 200 Training Loss: 1.726e-01 Loss in Target Net: 3.142e-02 |
2020-02-04 00:48:13 Iteration 250 Training Loss: 1.675e-01 Loss in Target Net: 3.159e-02 |
2020-02-04 00:51:22 Iteration 300 Training Loss: 1.668e-01 Loss in Target Net: 3.794e-02 |
2020-02-04 00:54:32 Iteration 350 Training Loss: 1.625e-01 Loss in Target Net: 3.963e-02 |
2020-02-04 00:57:47 Iteration 400 Training Loss: 1.623e-01 Loss in Target Net: 4.264e-02 |
2020-02-04 01:00:59 Iteration 450 Training Loss: 1.593e-01 Loss in Target Net: 3.747e-02 |
2020-02-04 01:04:13 Iteration 500 Training Loss: 1.591e-01 Loss in Target Net: 4.107e-02 |
2020-02-04 01:07:25 Iteration 550 Training Loss: 1.573e-01 Loss in Target Net: 3.669e-02 |
2020-02-04 01:10:39 Iteration 600 Training Loss: 1.575e-01 Loss in Target Net: 3.535e-02 |
2020-02-04 01:13:52 Iteration 650 Training Loss: 1.569e-01 Loss in Target Net: 3.812e-02 |
2020-02-04 01:17:05 Iteration 700 Training Loss: 1.535e-01 Loss in Target Net: 3.756e-02 |
2020-02-04 01:20:18 Iteration 750 Training Loss: 1.584e-01 Loss in Target Net: 3.923e-02 |
2020-02-04 01:23:32 Iteration 800 Training Loss: 1.585e-01 Loss in Target Net: 3.849e-02 |
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