text stringlengths 5 1.13k |
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SENet18 0 |
ResNet50 0 |
ResNeXt29_2x64d 0 |
GoogLeNet 0 |
MobileNetV2 0 |
ResNet18 0 |
DenseNet121 0 |
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=46, 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/4000/46 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 21:26:26 Iteration 0 Training Loss: 1.082e+00 Loss in Target Net: 4.347e-01 |
2020-02-04 21:27:43 Iteration 50 Training Loss: 8.258e-02 Loss in Target Net: 2.059e-02 |
2020-02-04 21:29:00 Iteration 100 Training Loss: 6.854e-02 Loss in Target Net: 1.635e-02 |
2020-02-04 21:30:17 Iteration 150 Training Loss: 6.924e-02 Loss in Target Net: 1.757e-02 |
2020-02-04 21:31:34 Iteration 200 Training Loss: 6.900e-02 Loss in Target Net: 1.360e-02 |
2020-02-04 21:32:51 Iteration 250 Training Loss: 6.578e-02 Loss in Target Net: 1.563e-02 |
2020-02-04 21:34:08 Iteration 300 Training Loss: 6.578e-02 Loss in Target Net: 1.434e-02 |
2020-02-04 21:35:26 Iteration 350 Training Loss: 6.766e-02 Loss in Target Net: 1.570e-02 |
2020-02-04 21:36:42 Iteration 400 Training Loss: 6.444e-02 Loss in Target Net: 1.829e-02 |
2020-02-04 21:38:00 Iteration 450 Training Loss: 6.419e-02 Loss in Target Net: 2.415e-02 |
2020-02-04 21:39:16 Iteration 500 Training Loss: 6.049e-02 Loss in Target Net: 1.881e-02 |
2020-02-04 21:40:38 Iteration 550 Training Loss: 6.713e-02 Loss in Target Net: 2.668e-02 |
2020-02-04 21:42:08 Iteration 600 Training Loss: 6.940e-02 Loss in Target Net: 1.499e-02 |
2020-02-04 21:43:37 Iteration 650 Training Loss: 6.788e-02 Loss in Target Net: 2.256e-02 |
2020-02-04 21:45:07 Iteration 700 Training Loss: 6.429e-02 Loss in Target Net: 2.261e-02 |
2020-02-04 21:46:37 Iteration 750 Training Loss: 7.077e-02 Loss in Target Net: 2.076e-02 |
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2020-02-04 21:52:22 Iteration 950 Training Loss: 6.387e-02 Loss in Target Net: 2.197e-02 |
2020-02-04 21:53:46 Iteration 1000 Training Loss: 6.260e-02 Loss in Target Net: 1.953e-02 |
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2020-02-04 22:00:42 Iteration 1250 Training Loss: 6.096e-02 Loss in Target Net: 1.789e-02 |
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2020-02-04 22:11:42 Iteration 1650 Training Loss: 6.670e-02 Loss in Target Net: 1.920e-02 |
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2020-02-04 22:14:24 Iteration 1750 Training Loss: 6.189e-02 Loss in Target Net: 2.531e-02 |
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2020-02-04 22:21:45 Iteration 2000 Training Loss: 5.901e-02 Loss in Target Net: 2.646e-02 |
2020-02-04 22:23:16 Iteration 2050 Training Loss: 6.445e-02 Loss in Target Net: 2.181e-02 |
2020-02-04 22:24:45 Iteration 2100 Training Loss: 6.910e-02 Loss in Target Net: 1.958e-02 |
2020-02-04 22:26:12 Iteration 2150 Training Loss: 6.580e-02 Loss in Target Net: 2.197e-02 |
2020-02-04 22:27:39 Iteration 2200 Training Loss: 6.256e-02 Loss in Target Net: 1.964e-02 |
2020-02-04 22:29:04 Iteration 2250 Training Loss: 6.066e-02 Loss in Target Net: 2.314e-02 |
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2020-02-04 22:31:53 Iteration 2350 Training Loss: 6.570e-02 Loss in Target Net: 2.278e-02 |
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2020-02-04 22:34:47 Iteration 2450 Training Loss: 6.562e-02 Loss in Target Net: 2.360e-02 |
2020-02-04 22:36:13 Iteration 2500 Training Loss: 5.974e-02 Loss in Target Net: 1.978e-02 |
2020-02-04 22:37:41 Iteration 2550 Training Loss: 6.600e-02 Loss in Target Net: 1.513e-02 |
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2020-02-04 22:40:34 Iteration 2650 Training Loss: 6.255e-02 Loss in Target Net: 1.939e-02 |
2020-02-04 22:41:58 Iteration 2700 Training Loss: 6.443e-02 Loss in Target Net: 2.064e-02 |
2020-02-04 22:43:25 Iteration 2750 Training Loss: 6.296e-02 Loss in Target Net: 2.020e-02 |
2020-02-04 22:44:48 Iteration 2800 Training Loss: 6.688e-02 Loss in Target Net: 2.071e-02 |
2020-02-04 22:46:10 Iteration 2850 Training Loss: 6.665e-02 Loss in Target Net: 2.328e-02 |
2020-02-04 22:47:29 Iteration 2900 Training Loss: 5.873e-02 Loss in Target Net: 1.550e-02 |
2020-02-04 22:48:54 Iteration 2950 Training Loss: 6.396e-02 Loss in Target Net: 1.894e-02 |
2020-02-04 22:50:20 Iteration 3000 Training Loss: 6.107e-02 Loss in Target Net: 1.907e-02 |
2020-02-04 22:51:46 Iteration 3050 Training Loss: 6.618e-02 Loss in Target Net: 1.930e-02 |
2020-02-04 22:53:12 Iteration 3100 Training Loss: 6.559e-02 Loss in Target Net: 2.225e-02 |
2020-02-04 22:54:38 Iteration 3150 Training Loss: 6.401e-02 Loss in Target Net: 2.335e-02 |
2020-02-04 22:56:07 Iteration 3200 Training Loss: 6.350e-02 Loss in Target Net: 1.855e-02 |
2020-02-04 22:57:35 Iteration 3250 Training Loss: 6.364e-02 Loss in Target Net: 1.518e-02 |
2020-02-04 22:59:02 Iteration 3300 Training Loss: 6.469e-02 Loss in Target Net: 2.036e-02 |
2020-02-04 23:00:28 Iteration 3350 Training Loss: 6.440e-02 Loss in Target Net: 2.438e-02 |
2020-02-04 23:01:54 Iteration 3400 Training Loss: 6.978e-02 Loss in Target Net: 1.099e-02 |
2020-02-04 23:03:20 Iteration 3450 Training Loss: 6.107e-02 Loss in Target Net: 2.184e-02 |
2020-02-04 23:04:45 Iteration 3500 Training Loss: 6.269e-02 Loss in Target Net: 1.989e-02 |
2020-02-04 23:06:08 Iteration 3550 Training Loss: 6.622e-02 Loss in Target Net: 2.509e-02 |
2020-02-04 23:07:31 Iteration 3600 Training Loss: 6.061e-02 Loss in Target Net: 2.544e-02 |
2020-02-04 23:08:54 Iteration 3650 Training Loss: 6.240e-02 Loss in Target Net: 2.373e-02 |
2020-02-04 23:10:18 Iteration 3700 Training Loss: 6.757e-02 Loss in Target Net: 2.163e-02 |
2020-02-04 23:11:43 Iteration 3750 Training Loss: 6.152e-02 Loss in Target Net: 2.272e-02 |
2020-02-04 23:13:04 Iteration 3800 Training Loss: 6.303e-02 Loss in Target Net: 1.894e-02 |
2020-02-04 23:14:23 Iteration 3850 Training Loss: 7.042e-02 Loss in Target Net: 1.458e-02 |
2020-02-04 23:15:43 Iteration 3900 Training Loss: 6.424e-02 Loss in Target Net: 1.802e-02 |
2020-02-04 23:17:02 Iteration 3950 Training Loss: 6.385e-02 Loss in Target Net: 1.729e-02 |
2020-02-04 23:18:18 Iteration 3999 Training Loss: 5.874e-02 Loss in Target Net: 2.130e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:18:37, Epoch 0, Iteration 7, loss 0.615 (2.644), acc 94.231 (78.000) |
2020-02-04 23:18:37, Epoch 30, Iteration 7, loss 0.212 (0.157), acc 96.154 (97.200) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-13.279111, -30.027763, -59.734894, -4.504331, -49.288803, -9.96828, 21.410795, -58.794792, 25.062456, -116.57166], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 23:19:07 Epoch 59, Val iteration 0, acc 91.000 (91.000) |
2020-02-04 23:19:55 Epoch 59, Val iteration 19, acc 92.200 (91.880) |
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