text stringlengths 5 1.13k |
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MobileNetV2 0 |
ResNet18 1 |
DenseNet121 1 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='2', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=4000, 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=0.1, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0, 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', 'GoogLeNet', 'MobileNetV2'], target_index=2, target_label=6, target_net=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d', 'GoogLeNet', 'MobileNetV2', 'ResNet18', 'DenseNet121'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-ourmean/mean/4000/2 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 17:11:24 Iteration 0 Training Loss: 1.104e+00 Loss in Target Net: 4.230e-01 |
2020-01-31 17:11:46 Iteration 50 Training Loss: 9.240e-02 Loss in Target Net: 9.178e-03 |
2020-01-31 17:12:09 Iteration 100 Training Loss: 8.604e-02 Loss in Target Net: 1.045e-02 |
2020-01-31 17:12:31 Iteration 150 Training Loss: 8.177e-02 Loss in Target Net: 7.776e-03 |
2020-01-31 17:12:53 Iteration 200 Training Loss: 7.904e-02 Loss in Target Net: 6.451e-03 |
2020-01-31 17:13:16 Iteration 250 Training Loss: 7.844e-02 Loss in Target Net: 7.890e-03 |
2020-01-31 17:13:38 Iteration 300 Training Loss: 8.801e-02 Loss in Target Net: 8.106e-03 |
2020-01-31 17:14:01 Iteration 350 Training Loss: 8.189e-02 Loss in Target Net: 6.156e-03 |
2020-01-31 17:14:25 Iteration 400 Training Loss: 8.534e-02 Loss in Target Net: 5.227e-03 |
2020-01-31 17:14:48 Iteration 450 Training Loss: 7.864e-02 Loss in Target Net: 6.838e-03 |
2020-01-31 17:15:10 Iteration 500 Training Loss: 8.385e-02 Loss in Target Net: 5.221e-03 |
2020-01-31 17:15:33 Iteration 550 Training Loss: 8.232e-02 Loss in Target Net: 6.653e-03 |
2020-01-31 17:15:55 Iteration 600 Training Loss: 8.240e-02 Loss in Target Net: 7.957e-03 |
2020-01-31 17:16:17 Iteration 650 Training Loss: 7.487e-02 Loss in Target Net: 8.617e-03 |
2020-01-31 17:16:40 Iteration 700 Training Loss: 7.007e-02 Loss in Target Net: 1.364e-02 |
2020-01-31 17:17:03 Iteration 750 Training Loss: 7.914e-02 Loss in Target Net: 1.095e-02 |
2020-01-31 17:17:25 Iteration 800 Training Loss: 7.675e-02 Loss in Target Net: 8.790e-03 |
2020-01-31 17:17:47 Iteration 850 Training Loss: 8.038e-02 Loss in Target Net: 8.803e-03 |
2020-01-31 17:18:10 Iteration 900 Training Loss: 7.548e-02 Loss in Target Net: 6.400e-03 |
2020-01-31 17:18:32 Iteration 950 Training Loss: 7.372e-02 Loss in Target Net: 1.194e-02 |
2020-01-31 17:18:55 Iteration 1000 Training Loss: 7.459e-02 Loss in Target Net: 6.178e-03 |
2020-01-31 17:19:17 Iteration 1050 Training Loss: 7.242e-02 Loss in Target Net: 6.803e-03 |
2020-01-31 17:19:41 Iteration 1100 Training Loss: 7.841e-02 Loss in Target Net: 5.829e-03 |
2020-01-31 17:20:03 Iteration 1150 Training Loss: 7.990e-02 Loss in Target Net: 9.151e-03 |
2020-01-31 17:20:26 Iteration 1200 Training Loss: 7.699e-02 Loss in Target Net: 8.534e-03 |
2020-01-31 17:20:48 Iteration 1250 Training Loss: 7.707e-02 Loss in Target Net: 8.906e-03 |
2020-01-31 17:21:11 Iteration 1300 Training Loss: 8.186e-02 Loss in Target Net: 7.797e-03 |
2020-01-31 17:21:33 Iteration 1350 Training Loss: 7.527e-02 Loss in Target Net: 7.604e-03 |
2020-01-31 17:21:56 Iteration 1400 Training Loss: 7.451e-02 Loss in Target Net: 8.850e-03 |
2020-01-31 17:22:19 Iteration 1450 Training Loss: 8.216e-02 Loss in Target Net: 1.168e-02 |
2020-01-31 17:22:41 Iteration 1500 Training Loss: 7.509e-02 Loss in Target Net: 1.008e-02 |
2020-01-31 17:23:04 Iteration 1550 Training Loss: 7.714e-02 Loss in Target Net: 8.351e-03 |
2020-01-31 17:23:26 Iteration 1600 Training Loss: 7.142e-02 Loss in Target Net: 7.249e-03 |
2020-01-31 17:23:48 Iteration 1650 Training Loss: 7.729e-02 Loss in Target Net: 7.976e-03 |
2020-01-31 17:24:10 Iteration 1700 Training Loss: 7.626e-02 Loss in Target Net: 5.410e-03 |
2020-01-31 17:24:33 Iteration 1750 Training Loss: 7.767e-02 Loss in Target Net: 6.259e-03 |
2020-01-31 17:24:55 Iteration 1800 Training Loss: 8.030e-02 Loss in Target Net: 1.034e-02 |
2020-01-31 17:25:17 Iteration 1850 Training Loss: 6.681e-02 Loss in Target Net: 9.878e-03 |
2020-01-31 17:25:40 Iteration 1900 Training Loss: 7.486e-02 Loss in Target Net: 9.954e-03 |
2020-01-31 17:26:02 Iteration 1950 Training Loss: 7.445e-02 Loss in Target Net: 6.671e-03 |
2020-01-31 17:26:24 Iteration 2000 Training Loss: 7.437e-02 Loss in Target Net: 1.030e-02 |
2020-01-31 17:26:46 Iteration 2050 Training Loss: 7.380e-02 Loss in Target Net: 9.474e-03 |
2020-01-31 17:27:08 Iteration 2100 Training Loss: 7.242e-02 Loss in Target Net: 8.278e-03 |
2020-01-31 17:27:31 Iteration 2150 Training Loss: 7.736e-02 Loss in Target Net: 7.381e-03 |
2020-01-31 17:27:53 Iteration 2200 Training Loss: 7.015e-02 Loss in Target Net: 8.336e-03 |
2020-01-31 17:28:15 Iteration 2250 Training Loss: 7.547e-02 Loss in Target Net: 5.902e-03 |
2020-01-31 17:28:37 Iteration 2300 Training Loss: 8.676e-02 Loss in Target Net: 6.732e-03 |
2020-01-31 17:28:59 Iteration 2350 Training Loss: 7.474e-02 Loss in Target Net: 8.414e-03 |
2020-01-31 17:29:22 Iteration 2400 Training Loss: 7.698e-02 Loss in Target Net: 1.205e-02 |
2020-01-31 17:29:45 Iteration 2450 Training Loss: 7.348e-02 Loss in Target Net: 1.144e-02 |
2020-01-31 17:30:07 Iteration 2500 Training Loss: 7.319e-02 Loss in Target Net: 8.188e-03 |
2020-01-31 17:30:30 Iteration 2550 Training Loss: 8.254e-02 Loss in Target Net: 1.778e-02 |
2020-01-31 17:30:52 Iteration 2600 Training Loss: 7.335e-02 Loss in Target Net: 1.448e-02 |
2020-01-31 17:31:15 Iteration 2650 Training Loss: 7.973e-02 Loss in Target Net: 7.653e-03 |
2020-01-31 17:31:38 Iteration 2700 Training Loss: 7.459e-02 Loss in Target Net: 8.785e-03 |
2020-01-31 17:32:00 Iteration 2750 Training Loss: 7.372e-02 Loss in Target Net: 7.305e-03 |
2020-01-31 17:32:23 Iteration 2800 Training Loss: 6.987e-02 Loss in Target Net: 6.688e-03 |
2020-01-31 17:32:46 Iteration 2850 Training Loss: 7.454e-02 Loss in Target Net: 1.088e-02 |
2020-01-31 17:33:08 Iteration 2900 Training Loss: 7.562e-02 Loss in Target Net: 8.488e-03 |
2020-01-31 17:33:30 Iteration 2950 Training Loss: 7.561e-02 Loss in Target Net: 9.431e-03 |
2020-01-31 17:33:53 Iteration 3000 Training Loss: 8.223e-02 Loss in Target Net: 1.101e-02 |
2020-01-31 17:34:15 Iteration 3050 Training Loss: 7.692e-02 Loss in Target Net: 7.231e-03 |
2020-01-31 17:34:38 Iteration 3100 Training Loss: 7.518e-02 Loss in Target Net: 8.771e-03 |
2020-01-31 17:35:00 Iteration 3150 Training Loss: 7.235e-02 Loss in Target Net: 1.261e-02 |
2020-01-31 17:35:22 Iteration 3200 Training Loss: 7.788e-02 Loss in Target Net: 8.145e-03 |
2020-01-31 17:35:46 Iteration 3250 Training Loss: 7.716e-02 Loss in Target Net: 7.381e-03 |
2020-01-31 17:36:08 Iteration 3300 Training Loss: 7.652e-02 Loss in Target Net: 8.339e-03 |
2020-01-31 17:36:31 Iteration 3350 Training Loss: 7.835e-02 Loss in Target Net: 7.092e-03 |
2020-01-31 17:36:54 Iteration 3400 Training Loss: 7.628e-02 Loss in Target Net: 8.701e-03 |
2020-01-31 17:37:16 Iteration 3450 Training Loss: 7.129e-02 Loss in Target Net: 8.296e-03 |
2020-01-31 17:37:39 Iteration 3500 Training Loss: 7.449e-02 Loss in Target Net: 6.021e-03 |
2020-01-31 17:38:01 Iteration 3550 Training Loss: 7.551e-02 Loss in Target Net: 4.554e-03 |
2020-01-31 17:38:23 Iteration 3600 Training Loss: 7.648e-02 Loss in Target Net: 9.299e-03 |
2020-01-31 17:38:46 Iteration 3650 Training Loss: 7.066e-02 Loss in Target Net: 8.453e-03 |
2020-01-31 17:39:08 Iteration 3700 Training Loss: 7.582e-02 Loss in Target Net: 1.316e-02 |
2020-01-31 17:39:31 Iteration 3750 Training Loss: 7.704e-02 Loss in Target Net: 1.128e-02 |
2020-01-31 17:39:53 Iteration 3800 Training Loss: 7.379e-02 Loss in Target Net: 5.877e-03 |
2020-01-31 17:40:15 Iteration 3850 Training Loss: 7.564e-02 Loss in Target Net: 7.606e-03 |
2020-01-31 17:40:38 Iteration 3900 Training Loss: 7.317e-02 Loss in Target Net: 8.149e-03 |
2020-01-31 17:41:00 Iteration 3950 Training Loss: 7.760e-02 Loss in Target Net: 5.392e-03 |
2020-01-31 17:41:22 Iteration 3999 Training Loss: 7.398e-02 Loss in Target Net: 5.353e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:41:26, Epoch 0, Iteration 7, loss 1.950 (4.367), acc 88.462 (65.000) |
2020-01-31 17:41:26, Epoch 30, Iteration 7, loss 0.387 (0.213), acc 94.231 (96.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[38.592724, 9.063739, -21.974857, 22.932188, -6.846222, 15.399769, 50.117035, -50.930023, 45.619255, -77.94589], Poisons' Predictions:[8, 8, 8, 6, 6] |
2020-01-31 17:41:30 Epoch 59, Val iteration 0, acc 90.200 (90.200) |
2020-01-31 17:41:38 Epoch 59, Val iteration 19, acc 92.600 (91.860) |
* Prec: 91.86000213623046 |
-------- |
SENet18 |
Using Adam for retraining |
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