text stringlengths 5 1.13k |
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2020-02-04 23:25:19 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-04 23:25:39 Epoch 59, Val iteration 19, acc 92.600 (93.120) |
* Prec: 93.12000160217285 |
-------- |
GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:25:48, Epoch 0, Iteration 7, loss 0.088 (0.406), acc 94.231 (89.200) |
2020-02-04 23:25:48, Epoch 30, Iteration 7, loss 0.137 (0.086), acc 94.231 (97.200) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-15.604692, -14.204229, -14.707011, -3.3630495, -13.845627, -8.983503, 10.23419, -9.04417, 12.214141, -15.218464], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 23:26:03 Epoch 59, Val iteration 0, acc 91.400 (91.400) |
2020-02-04 23:26:31 Epoch 59, Val iteration 19, acc 92.200 (92.190) |
* Prec: 92.19000091552735 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:26:35, Epoch 0, Iteration 7, loss 2.320 (3.419), acc 71.154 (60.000) |
2020-02-04 23:26:36, Epoch 30, Iteration 7, loss 0.877 (0.478), acc 88.462 (91.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[2.5519843, 6.765134, -11.879494, 15.456415, -7.6777954, -11.594884, 27.186754, -29.383738, 27.845661, -30.567335], Poisons' Predictions:[6, 8, 8, 8, 8] |
2020-02-04 23:26:39 Epoch 59, Val iteration 0, acc 87.800 (87.800) |
2020-02-04 23:26:46 Epoch 59, Val iteration 19, acc 87.000 (86.670) |
* Prec: 86.67000198364258 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:26:49, Epoch 0, Iteration 7, loss 0.863 (0.726), acc 90.385 (87.000) |
2020-02-04 23:26:50, Epoch 30, Iteration 7, loss 0.017 (0.081), acc 100.000 (98.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-20.895285, 0.6106057, -8.129458, 4.180918, -35.79907, -4.4581876, 15.789268, -10.264192, 13.622942, -20.298737], Poisons' Predictions:[8, 6, 8, 8, 8] |
2020-02-04 23:26:50 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-02-04 23:26:56 Epoch 59, Val iteration 19, acc 93.600 (92.550) |
* Prec: 92.55000152587891 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:27:04, Epoch 0, Iteration 7, loss 0.261 (0.362), acc 92.308 (93.000) |
2020-02-04 23:27:05, Epoch 30, Iteration 7, loss 0.002 (0.005), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-10.156627, -8.037192, -9.197665, -3.6539621, -13.6870575, -3.8635335, 6.4960933, -33.12011, 7.4709625, -16.665794], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 23:27:15 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 23:27:38 Epoch 59, Val iteration 19, acc 92.800 (92.850) |
* Prec: 92.85000228881836 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 121 |
TARGET INDEX: 43 |
DPN92 0 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 0 |
GoogLeNet 1 |
MobileNetV2 1 |
ResNet18 0 |
DenseNet121 1 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='12', 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=44, 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/4000/44 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 21:21:41 Iteration 0 Training Loss: 1.201e+00 Loss in Target Net: 4.065e-01 |
2020-02-04 21:22:58 Iteration 50 Training Loss: 1.282e-01 Loss in Target Net: 1.511e-02 |
2020-02-04 21:24:16 Iteration 100 Training Loss: 1.074e-01 Loss in Target Net: 9.169e-03 |
2020-02-04 21:25:33 Iteration 150 Training Loss: 1.051e-01 Loss in Target Net: 7.395e-03 |
2020-02-04 21:26:51 Iteration 200 Training Loss: 9.834e-02 Loss in Target Net: 9.175e-03 |
2020-02-04 21:28:09 Iteration 250 Training Loss: 1.039e-01 Loss in Target Net: 1.165e-02 |
2020-02-04 21:29:28 Iteration 300 Training Loss: 9.350e-02 Loss in Target Net: 8.249e-03 |
2020-02-04 21:30:46 Iteration 350 Training Loss: 1.028e-01 Loss in Target Net: 9.480e-03 |
2020-02-04 21:32:05 Iteration 400 Training Loss: 9.990e-02 Loss in Target Net: 1.230e-02 |
2020-02-04 21:33:25 Iteration 450 Training Loss: 9.913e-02 Loss in Target Net: 1.310e-02 |
2020-02-04 21:34:46 Iteration 500 Training Loss: 9.418e-02 Loss in Target Net: 1.321e-02 |
2020-02-04 21:36:05 Iteration 550 Training Loss: 9.692e-02 Loss in Target Net: 1.378e-02 |
2020-02-04 21:37:23 Iteration 600 Training Loss: 9.529e-02 Loss in Target Net: 1.341e-02 |
2020-02-04 21:38:42 Iteration 650 Training Loss: 9.281e-02 Loss in Target Net: 1.226e-02 |
2020-02-04 21:40:04 Iteration 700 Training Loss: 9.273e-02 Loss in Target Net: 1.099e-02 |
2020-02-04 21:41:39 Iteration 750 Training Loss: 9.300e-02 Loss in Target Net: 1.008e-02 |
2020-02-04 21:43:17 Iteration 800 Training Loss: 9.390e-02 Loss in Target Net: 1.011e-02 |
2020-02-04 21:44:56 Iteration 850 Training Loss: 8.803e-02 Loss in Target Net: 1.051e-02 |
2020-02-04 21:46:34 Iteration 900 Training Loss: 8.431e-02 Loss in Target Net: 9.004e-03 |
2020-02-04 21:48:12 Iteration 950 Training Loss: 8.700e-02 Loss in Target Net: 1.140e-02 |
2020-02-04 21:49:47 Iteration 1000 Training Loss: 9.017e-02 Loss in Target Net: 9.786e-03 |
2020-02-04 21:51:18 Iteration 1050 Training Loss: 9.144e-02 Loss in Target Net: 9.227e-03 |
2020-02-04 21:52:51 Iteration 1100 Training Loss: 8.673e-02 Loss in Target Net: 9.998e-03 |
2020-02-04 21:54:23 Iteration 1150 Training Loss: 9.034e-02 Loss in Target Net: 9.729e-03 |
2020-02-04 21:55:56 Iteration 1200 Training Loss: 8.812e-02 Loss in Target Net: 1.188e-02 |
2020-02-04 21:57:27 Iteration 1250 Training Loss: 8.855e-02 Loss in Target Net: 8.417e-03 |
2020-02-04 21:58:55 Iteration 1300 Training Loss: 8.850e-02 Loss in Target Net: 1.225e-02 |
2020-02-04 22:00:22 Iteration 1350 Training Loss: 8.964e-02 Loss in Target Net: 1.151e-02 |
2020-02-04 22:01:49 Iteration 1400 Training Loss: 9.225e-02 Loss in Target Net: 9.547e-03 |
2020-02-04 22:03:17 Iteration 1450 Training Loss: 8.707e-02 Loss in Target Net: 1.000e-02 |
2020-02-04 22:04:46 Iteration 1500 Training Loss: 9.906e-02 Loss in Target Net: 1.090e-02 |
2020-02-04 22:06:16 Iteration 1550 Training Loss: 9.434e-02 Loss in Target Net: 1.236e-02 |
2020-02-04 22:07:43 Iteration 1600 Training Loss: 9.209e-02 Loss in Target Net: 1.293e-02 |
2020-02-04 22:09:10 Iteration 1650 Training Loss: 9.086e-02 Loss in Target Net: 1.337e-02 |
2020-02-04 22:10:35 Iteration 1700 Training Loss: 9.462e-02 Loss in Target Net: 1.013e-02 |
2020-02-04 22:12:00 Iteration 1750 Training Loss: 8.586e-02 Loss in Target Net: 1.608e-02 |
2020-02-04 22:13:25 Iteration 1800 Training Loss: 8.881e-02 Loss in Target Net: 1.158e-02 |
2020-02-04 22:14:52 Iteration 1850 Training Loss: 9.220e-02 Loss in Target Net: 1.325e-02 |
2020-02-04 22:16:23 Iteration 1900 Training Loss: 9.220e-02 Loss in Target Net: 1.130e-02 |
2020-02-04 22:17:55 Iteration 1950 Training Loss: 9.096e-02 Loss in Target Net: 9.750e-03 |
2020-02-04 22:19:34 Iteration 2000 Training Loss: 9.297e-02 Loss in Target Net: 8.046e-03 |
2020-02-04 22:21:12 Iteration 2050 Training Loss: 9.348e-02 Loss in Target Net: 9.283e-03 |
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