text stringlengths 5 1.13k |
|---|
GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:14:49, Epoch 0, Iteration 7, loss 0.997 (0.556), acc 82.692 (87.800) |
2020-01-31 18:14:49, Epoch 30, Iteration 7, loss 0.116 (0.059), acc 98.077 (97.600) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-12.55736, -5.9517612, -10.2868185, 0.5427823, -6.9124017, -7.807498, 5.671088, -3.3927329, 10.717001, -17.93443], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 18:14:52 Epoch 59, Val iteration 0, acc 91.200 (91.200) |
2020-01-31 18:14:56 Epoch 59, Val iteration 19, acc 91.200 (91.670) |
* Prec: 91.67000198364258 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:14:59, Epoch 0, Iteration 7, loss 2.081 (4.452), acc 78.846 (63.800) |
2020-01-31 18:14:59, Epoch 30, Iteration 7, loss 0.116 (0.350), acc 98.077 (93.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-16.036911, -4.4095125, -6.869385, 7.8163924, -27.462149, -12.549203, 20.953947, -45.696095, 18.134373, -12.4314785], Poisons' Predictions:[6, 8, 8, 8, 8] |
2020-01-31 18:15:00 Epoch 59, Val iteration 0, acc 88.800 (88.800) |
2020-01-31 18:15:02 Epoch 59, Val iteration 19, acc 88.000 (86.990) |
* Prec: 86.9900016784668 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:15:04, Epoch 0, Iteration 7, loss 0.202 (0.747), acc 96.154 (87.600) |
2020-01-31 18:15:04, Epoch 30, Iteration 7, loss 0.030 (0.026), acc 98.077 (99.200) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-30.599062, -16.075813, -8.620347, 4.655854, -40.720737, -6.9177628, 11.011714, -23.37335, 10.797763, -41.556435], Poisons' Predictions:[8, 6, 8, 6, 8] |
2020-01-31 18:15:04 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-01-31 18:15:06 Epoch 59, Val iteration 19, acc 93.600 (92.570) |
* Prec: 92.57000198364258 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:15:09, Epoch 0, Iteration 7, loss 0.070 (0.374), acc 98.077 (91.400) |
2020-01-31 18:15:09, Epoch 30, Iteration 7, loss 0.025 (0.011), acc 98.077 (99.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-7.218327, -13.772582, -13.474847, -2.085992, -2.9448407, -11.878392, 7.255373, -44.477863, 6.4128704, -20.386919], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 18:15:11 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-01-31 18:15:16 Epoch 59, Val iteration 19, acc 93.200 (93.080) |
* Prec: 93.08000144958496 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 30 |
TARGET INDEX: 4 |
DPN92 1 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
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='8', 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=40, 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/4000/40 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 21:22:39 Iteration 0 Training Loss: 1.047e+00 Loss in Target Net: 4.040e-01 |
2020-02-04 21:23:55 Iteration 50 Training Loss: 9.619e-02 Loss in Target Net: 7.189e-03 |
2020-02-04 21:25:11 Iteration 100 Training Loss: 8.044e-02 Loss in Target Net: 1.111e-02 |
2020-02-04 21:26:26 Iteration 150 Training Loss: 7.591e-02 Loss in Target Net: 7.649e-03 |
2020-02-04 21:27:43 Iteration 200 Training Loss: 7.819e-02 Loss in Target Net: 5.487e-03 |
2020-02-04 21:29:00 Iteration 250 Training Loss: 7.680e-02 Loss in Target Net: 7.466e-03 |
2020-02-04 21:30:17 Iteration 300 Training Loss: 7.592e-02 Loss in Target Net: 6.441e-03 |
2020-02-04 21:31:34 Iteration 350 Training Loss: 7.739e-02 Loss in Target Net: 6.497e-03 |
2020-02-04 21:32:51 Iteration 400 Training Loss: 7.247e-02 Loss in Target Net: 9.678e-03 |
2020-02-04 21:34:07 Iteration 450 Training Loss: 6.743e-02 Loss in Target Net: 6.659e-03 |
2020-02-04 21:35:24 Iteration 500 Training Loss: 7.236e-02 Loss in Target Net: 6.765e-03 |
2020-02-04 21:36:42 Iteration 550 Training Loss: 7.323e-02 Loss in Target Net: 7.614e-03 |
2020-02-04 21:37:58 Iteration 600 Training Loss: 6.626e-02 Loss in Target Net: 8.238e-03 |
2020-02-04 21:39:16 Iteration 650 Training Loss: 7.167e-02 Loss in Target Net: 8.321e-03 |
2020-02-04 21:40:43 Iteration 700 Training Loss: 6.918e-02 Loss in Target Net: 1.388e-02 |
2020-02-04 21:42:21 Iteration 750 Training Loss: 7.242e-02 Loss in Target Net: 9.511e-03 |
2020-02-04 21:43:59 Iteration 800 Training Loss: 7.204e-02 Loss in Target Net: 7.583e-03 |
2020-02-04 21:45:37 Iteration 850 Training Loss: 7.071e-02 Loss in Target Net: 7.706e-03 |
2020-02-04 21:47:15 Iteration 900 Training Loss: 7.379e-02 Loss in Target Net: 5.164e-03 |
2020-02-04 21:48:54 Iteration 950 Training Loss: 6.667e-02 Loss in Target Net: 7.976e-03 |
2020-02-04 21:50:24 Iteration 1000 Training Loss: 6.529e-02 Loss in Target Net: 7.364e-03 |
2020-02-04 21:51:53 Iteration 1050 Training Loss: 6.991e-02 Loss in Target Net: 1.019e-02 |
2020-02-04 21:53:25 Iteration 1100 Training Loss: 6.907e-02 Loss in Target Net: 6.668e-03 |
2020-02-04 21:54:57 Iteration 1150 Training Loss: 7.435e-02 Loss in Target Net: 1.148e-02 |
2020-02-04 21:56:27 Iteration 1200 Training Loss: 6.999e-02 Loss in Target Net: 9.349e-03 |
2020-02-04 21:57:54 Iteration 1250 Training Loss: 6.999e-02 Loss in Target Net: 6.197e-03 |
2020-02-04 21:59:21 Iteration 1300 Training Loss: 7.415e-02 Loss in Target Net: 6.680e-03 |
2020-02-04 22:00:48 Iteration 1350 Training Loss: 7.833e-02 Loss in Target Net: 7.848e-03 |
2020-02-04 22:02:14 Iteration 1400 Training Loss: 7.451e-02 Loss in Target Net: 7.032e-03 |
2020-02-04 22:03:41 Iteration 1450 Training Loss: 7.454e-02 Loss in Target Net: 7.888e-03 |
2020-02-04 22:05:09 Iteration 1500 Training Loss: 7.448e-02 Loss in Target Net: 8.386e-03 |
2020-02-04 22:06:39 Iteration 1550 Training Loss: 7.318e-02 Loss in Target Net: 7.004e-03 |
2020-02-04 22:08:03 Iteration 1600 Training Loss: 7.061e-02 Loss in Target Net: 9.613e-03 |
2020-02-04 22:09:31 Iteration 1650 Training Loss: 7.264e-02 Loss in Target Net: 4.896e-03 |
2020-02-04 22:10:55 Iteration 1700 Training Loss: 7.744e-02 Loss in Target Net: 4.957e-03 |
2020-02-04 22:12:20 Iteration 1750 Training Loss: 7.339e-02 Loss in Target Net: 5.295e-03 |
2020-02-04 22:13:43 Iteration 1800 Training Loss: 7.134e-02 Loss in Target Net: 5.547e-03 |
2020-02-04 22:15:09 Iteration 1850 Training Loss: 7.487e-02 Loss in Target Net: 5.192e-03 |
2020-02-04 22:16:40 Iteration 1900 Training Loss: 7.850e-02 Loss in Target Net: 5.851e-03 |
2020-02-04 22:18:14 Iteration 1950 Training Loss: 7.098e-02 Loss in Target Net: 6.905e-03 |
2020-02-04 22:19:51 Iteration 2000 Training Loss: 7.033e-02 Loss in Target Net: 6.557e-03 |
2020-02-04 22:21:29 Iteration 2050 Training Loss: 7.228e-02 Loss in Target Net: 6.419e-03 |
2020-02-04 22:23:09 Iteration 2100 Training Loss: 6.735e-02 Loss in Target Net: 7.020e-03 |
2020-02-04 22:24:46 Iteration 2150 Training Loss: 7.297e-02 Loss in Target Net: 5.637e-03 |
2020-02-04 22:26:20 Iteration 2200 Training Loss: 6.925e-02 Loss in Target Net: 6.834e-03 |
2020-02-04 22:27:54 Iteration 2250 Training Loss: 7.592e-02 Loss in Target Net: 9.699e-03 |
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