text stringlengths 5 1.13k |
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Files already downloaded and verified |
2020-01-31 21:14:26, Epoch 0, Iteration 7, loss 1.095 (3.631), acc 82.692 (59.800) |
2020-01-31 21:14:27, Epoch 30, Iteration 7, loss 0.205 (0.248), acc 96.154 (93.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-1.9551253, 8.346037, -15.359162, 11.992853, -30.62453, -11.673207, 16.016663, -3.824569, 22.66689, -46.190094], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:14:28 Epoch 59, Val iteration 0, acc 87.600 (87.600) |
2020-01-31 21:14:30 Epoch 59, Val iteration 19, acc 86.600 (87.010) |
* Prec: 87.01000175476074 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:14:31, Epoch 0, Iteration 7, loss 0.081 (1.066), acc 96.154 (82.600) |
2020-01-31 21:14:32, Epoch 30, Iteration 7, loss 0.056 (0.064), acc 98.077 (99.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-31.513891, -1.2192261, -18.790283, 4.733843, -33.678345, -3.1797059, 14.455039, -27.4409, 12.746192, -29.686676], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:14:32 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-01-31 21:14:34 Epoch 59, Val iteration 19, acc 93.400 (92.660) |
* Prec: 92.66000137329101 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:14:37, Epoch 0, Iteration 7, loss 0.177 (0.349), acc 94.231 (92.600) |
2020-01-31 21:14:37, Epoch 30, Iteration 7, loss 0.001 (0.002), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-7.50752, -22.998049, -24.437332, -10.288892, -4.5853615, -7.36671, 5.549059, -31.886469, 3.7264137, -22.315853], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:14:39 Epoch 59, Val iteration 0, acc 94.200 (94.200) |
2020-01-31 21:14:43 Epoch 59, Val iteration 19, acc 93.000 (93.080) |
* Prec: 93.08000221252442 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 29 |
DPN92 1 |
SENet18 0 |
ResNet50 0 |
ResNeXt29_2x64d 0 |
GoogLeNet 1 |
MobileNetV2 1 |
ResNet18 0 |
DenseNet121 0 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='3', 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=3, 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/4000/3 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 17:11:39 Iteration 0 Training Loss: 1.105e+00 Loss in Target Net: 4.165e-01 |
2020-01-31 17:11:59 Iteration 50 Training Loss: 8.977e-02 Loss in Target Net: 6.950e-03 |
2020-01-31 17:12:19 Iteration 100 Training Loss: 8.099e-02 Loss in Target Net: 8.502e-03 |
2020-01-31 17:12:40 Iteration 150 Training Loss: 7.474e-02 Loss in Target Net: 6.015e-03 |
2020-01-31 17:13:00 Iteration 200 Training Loss: 7.679e-02 Loss in Target Net: 9.612e-03 |
2020-01-31 17:13:21 Iteration 250 Training Loss: 7.235e-02 Loss in Target Net: 7.242e-03 |
2020-01-31 17:13:41 Iteration 300 Training Loss: 7.534e-02 Loss in Target Net: 4.908e-03 |
2020-01-31 17:14:01 Iteration 350 Training Loss: 7.057e-02 Loss in Target Net: 8.351e-03 |
2020-01-31 17:14:21 Iteration 400 Training Loss: 7.443e-02 Loss in Target Net: 1.208e-02 |
2020-01-31 17:14:41 Iteration 450 Training Loss: 7.733e-02 Loss in Target Net: 7.142e-03 |
2020-01-31 17:15:01 Iteration 500 Training Loss: 7.111e-02 Loss in Target Net: 4.924e-03 |
2020-01-31 17:15:21 Iteration 550 Training Loss: 7.794e-02 Loss in Target Net: 7.913e-03 |
2020-01-31 17:15:41 Iteration 600 Training Loss: 6.945e-02 Loss in Target Net: 7.158e-03 |
2020-01-31 17:16:01 Iteration 650 Training Loss: 6.843e-02 Loss in Target Net: 9.418e-03 |
2020-01-31 17:16:21 Iteration 700 Training Loss: 7.489e-02 Loss in Target Net: 1.001e-02 |
2020-01-31 17:16:41 Iteration 750 Training Loss: 6.702e-02 Loss in Target Net: 9.156e-03 |
2020-01-31 17:17:01 Iteration 800 Training Loss: 6.830e-02 Loss in Target Net: 1.056e-02 |
2020-01-31 17:17:21 Iteration 850 Training Loss: 6.971e-02 Loss in Target Net: 6.492e-03 |
2020-01-31 17:17:41 Iteration 900 Training Loss: 7.094e-02 Loss in Target Net: 1.084e-02 |
2020-01-31 17:18:01 Iteration 950 Training Loss: 7.339e-02 Loss in Target Net: 4.930e-03 |
2020-01-31 17:18:21 Iteration 1000 Training Loss: 6.623e-02 Loss in Target Net: 5.949e-03 |
2020-01-31 17:18:41 Iteration 1050 Training Loss: 6.978e-02 Loss in Target Net: 4.815e-03 |
2020-01-31 17:19:01 Iteration 1100 Training Loss: 7.676e-02 Loss in Target Net: 8.095e-03 |
2020-01-31 17:19:23 Iteration 1150 Training Loss: 6.579e-02 Loss in Target Net: 8.260e-03 |
2020-01-31 17:19:43 Iteration 1200 Training Loss: 6.861e-02 Loss in Target Net: 5.395e-03 |
2020-01-31 17:20:03 Iteration 1250 Training Loss: 7.125e-02 Loss in Target Net: 9.713e-03 |
2020-01-31 17:20:23 Iteration 1300 Training Loss: 7.482e-02 Loss in Target Net: 6.549e-03 |
2020-01-31 17:20:43 Iteration 1350 Training Loss: 6.688e-02 Loss in Target Net: 7.583e-03 |
2020-01-31 17:21:04 Iteration 1400 Training Loss: 6.592e-02 Loss in Target Net: 8.460e-03 |
2020-01-31 17:21:24 Iteration 1450 Training Loss: 6.831e-02 Loss in Target Net: 7.618e-03 |
2020-01-31 17:21:45 Iteration 1500 Training Loss: 6.957e-02 Loss in Target Net: 8.053e-03 |
2020-01-31 17:22:05 Iteration 1550 Training Loss: 6.593e-02 Loss in Target Net: 5.725e-03 |
2020-01-31 17:22:26 Iteration 1600 Training Loss: 6.733e-02 Loss in Target Net: 8.298e-03 |
2020-01-31 17:22:46 Iteration 1650 Training Loss: 6.798e-02 Loss in Target Net: 8.496e-03 |
2020-01-31 17:23:07 Iteration 1700 Training Loss: 7.101e-02 Loss in Target Net: 5.218e-03 |
2020-01-31 17:23:28 Iteration 1750 Training Loss: 7.166e-02 Loss in Target Net: 9.420e-03 |
2020-01-31 17:23:48 Iteration 1800 Training Loss: 7.246e-02 Loss in Target Net: 8.939e-03 |
2020-01-31 17:24:09 Iteration 1850 Training Loss: 7.192e-02 Loss in Target Net: 7.887e-03 |
2020-01-31 17:24:30 Iteration 1900 Training Loss: 6.612e-02 Loss in Target Net: 7.794e-03 |
2020-01-31 17:24:51 Iteration 1950 Training Loss: 6.747e-02 Loss in Target Net: 5.653e-03 |
2020-01-31 17:25:12 Iteration 2000 Training Loss: 6.834e-02 Loss in Target Net: 7.960e-03 |
2020-01-31 17:25:33 Iteration 2050 Training Loss: 7.596e-02 Loss in Target Net: 9.852e-03 |
2020-01-31 17:25:53 Iteration 2100 Training Loss: 6.966e-02 Loss in Target Net: 8.353e-03 |
2020-01-31 17:26:14 Iteration 2150 Training Loss: 6.888e-02 Loss in Target Net: 1.080e-02 |
2020-01-31 17:26:36 Iteration 2200 Training Loss: 6.802e-02 Loss in Target Net: 1.024e-02 |
2020-01-31 17:26:56 Iteration 2250 Training Loss: 6.934e-02 Loss in Target Net: 9.161e-03 |
2020-01-31 17:27:17 Iteration 2300 Training Loss: 6.613e-02 Loss in Target Net: 5.809e-03 |
2020-01-31 17:27:38 Iteration 2350 Training Loss: 7.401e-02 Loss in Target Net: 5.203e-03 |
2020-01-31 17:27:58 Iteration 2400 Training Loss: 6.484e-02 Loss in Target Net: 6.893e-03 |
2020-01-31 17:28:19 Iteration 2450 Training Loss: 6.706e-02 Loss in Target Net: 8.615e-03 |
2020-01-31 17:28:39 Iteration 2500 Training Loss: 7.374e-02 Loss in Target Net: 1.012e-02 |
2020-01-31 17:29:00 Iteration 2550 Training Loss: 6.976e-02 Loss in Target Net: 8.183e-03 |
2020-01-31 17:29:21 Iteration 2600 Training Loss: 6.651e-02 Loss in Target Net: 7.146e-03 |
2020-01-31 17:29:41 Iteration 2650 Training Loss: 6.845e-02 Loss in Target Net: 8.683e-03 |
2020-01-31 17:30:02 Iteration 2700 Training Loss: 6.866e-02 Loss in Target Net: 8.733e-03 |
2020-01-31 17:30:22 Iteration 2750 Training Loss: 6.825e-02 Loss in Target Net: 7.373e-03 |
2020-01-31 17:30:42 Iteration 2800 Training Loss: 6.882e-02 Loss in Target Net: 7.555e-03 |
2020-01-31 17:31:03 Iteration 2850 Training Loss: 6.793e-02 Loss in Target Net: 9.106e-03 |
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