text stringlengths 5 1.13k |
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2020-01-31 22:15:25, Epoch 30, Iteration 7, loss 0.011 (0.053), acc 100.000 (98.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-19.795284, -8.111228, -10.408763, -0.78592014, -5.5736465, -2.203908, 6.190131, -14.079952, 6.1450906, -15.3154745], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 22:15:27 Epoch 59, Val iteration 0, acc 92.000 (92.000) |
2020-01-31 22:15:32 Epoch 59, Val iteration 19, acc 92.200 (92.350) |
* Prec: 92.35000152587891 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 22:15:34, Epoch 0, Iteration 7, loss 1.658 (3.780), acc 82.692 (59.400) |
2020-01-31 22:15:34, Epoch 30, Iteration 7, loss 0.127 (0.240), acc 98.077 (94.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-9.94338, -10.913552, 3.1714268, 15.510607, 8.338046, -5.757491, 21.35048, -45.539753, 18.682844, -13.852976], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 22:15:35 Epoch 59, Val iteration 0, acc 89.200 (89.200) |
2020-01-31 22:15:37 Epoch 59, Val iteration 19, acc 89.600 (87.370) |
* Prec: 87.37000198364258 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 22:15:39, Epoch 0, Iteration 7, loss 0.213 (0.517), acc 98.077 (90.000) |
2020-01-31 22:15:39, Epoch 30, Iteration 7, loss 0.016 (0.022), acc 98.077 (99.200) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-60.13597, -11.93256, -19.615223, 0.2536657, -39.87067, -1.4359063, 10.608202, -27.637833, 12.867015, -36.12686], Poisons' Predictions:[6, 8, 8, 8, 8] |
2020-01-31 22:15:40 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-01-31 22:15:42 Epoch 59, Val iteration 19, acc 94.400 (92.650) |
* Prec: 92.6500015258789 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 22:15:44, Epoch 0, Iteration 7, loss 0.163 (0.366), acc 96.154 (93.800) |
2020-01-31 22:15:45, Epoch 30, Iteration 7, loss 0.003 (0.002), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-15.580541, -24.443726, -12.516415, -3.2762468, -20.65469, -11.353354, 2.4649568, -48.30389, 2.7277882, -16.674528], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 22:15:46 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 22:15:51 Epoch 59, Val iteration 19, acc 93.800 (93.070) |
* Prec: 93.0700008392334 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 27 |
TARGET INDEX: 36 |
DPN92 1 |
SENet18 1 |
ResNet50 1 |
ResNeXt29_2x64d 0 |
GoogLeNet 0 |
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='1', 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=37, 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/4000/37 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 21:46:22 Iteration 0 Training Loss: 1.024e+00 Loss in Target Net: 3.936e-01 |
2020-01-31 21:46:44 Iteration 50 Training Loss: 8.819e-02 Loss in Target Net: 2.607e-02 |
2020-01-31 21:47:07 Iteration 100 Training Loss: 6.957e-02 Loss in Target Net: 2.436e-02 |
2020-01-31 21:47:29 Iteration 150 Training Loss: 6.923e-02 Loss in Target Net: 2.471e-02 |
2020-01-31 21:47:51 Iteration 200 Training Loss: 6.941e-02 Loss in Target Net: 1.953e-02 |
2020-01-31 21:48:13 Iteration 250 Training Loss: 6.405e-02 Loss in Target Net: 2.640e-02 |
2020-01-31 21:48:36 Iteration 300 Training Loss: 6.693e-02 Loss in Target Net: 1.955e-02 |
2020-01-31 21:48:58 Iteration 350 Training Loss: 6.628e-02 Loss in Target Net: 2.317e-02 |
2020-01-31 21:49:20 Iteration 400 Training Loss: 6.648e-02 Loss in Target Net: 2.592e-02 |
2020-01-31 21:49:43 Iteration 450 Training Loss: 6.932e-02 Loss in Target Net: 2.745e-02 |
2020-01-31 21:50:06 Iteration 500 Training Loss: 6.864e-02 Loss in Target Net: 2.887e-02 |
2020-01-31 21:50:30 Iteration 550 Training Loss: 6.490e-02 Loss in Target Net: 2.176e-02 |
2020-01-31 21:50:52 Iteration 600 Training Loss: 6.511e-02 Loss in Target Net: 2.197e-02 |
2020-01-31 21:51:16 Iteration 650 Training Loss: 6.530e-02 Loss in Target Net: 2.458e-02 |
2020-01-31 21:51:38 Iteration 700 Training Loss: 6.111e-02 Loss in Target Net: 2.689e-02 |
2020-01-31 21:52:02 Iteration 750 Training Loss: 6.702e-02 Loss in Target Net: 2.331e-02 |
2020-01-31 21:52:25 Iteration 800 Training Loss: 6.218e-02 Loss in Target Net: 2.156e-02 |
2020-01-31 21:52:48 Iteration 850 Training Loss: 6.728e-02 Loss in Target Net: 2.956e-02 |
2020-01-31 21:53:14 Iteration 900 Training Loss: 6.312e-02 Loss in Target Net: 2.699e-02 |
2020-01-31 21:53:37 Iteration 950 Training Loss: 7.206e-02 Loss in Target Net: 3.007e-02 |
2020-01-31 21:53:59 Iteration 1000 Training Loss: 6.393e-02 Loss in Target Net: 2.976e-02 |
2020-01-31 21:54:22 Iteration 1050 Training Loss: 6.517e-02 Loss in Target Net: 1.968e-02 |
2020-01-31 21:54:44 Iteration 1100 Training Loss: 7.197e-02 Loss in Target Net: 3.002e-02 |
2020-01-31 21:55:07 Iteration 1150 Training Loss: 6.643e-02 Loss in Target Net: 2.363e-02 |
2020-01-31 21:55:29 Iteration 1200 Training Loss: 7.105e-02 Loss in Target Net: 2.761e-02 |
2020-01-31 21:55:51 Iteration 1250 Training Loss: 6.449e-02 Loss in Target Net: 2.124e-02 |
2020-01-31 21:56:15 Iteration 1300 Training Loss: 6.639e-02 Loss in Target Net: 2.058e-02 |
2020-01-31 21:56:39 Iteration 1350 Training Loss: 6.640e-02 Loss in Target Net: 1.891e-02 |
2020-01-31 21:57:03 Iteration 1400 Training Loss: 6.799e-02 Loss in Target Net: 3.147e-02 |
2020-01-31 21:57:26 Iteration 1450 Training Loss: 6.509e-02 Loss in Target Net: 2.681e-02 |
2020-01-31 21:57:49 Iteration 1500 Training Loss: 6.454e-02 Loss in Target Net: 2.955e-02 |
2020-01-31 21:58:12 Iteration 1550 Training Loss: 6.039e-02 Loss in Target Net: 2.900e-02 |
2020-01-31 21:58:35 Iteration 1600 Training Loss: 6.822e-02 Loss in Target Net: 2.292e-02 |
2020-01-31 21:58:57 Iteration 1650 Training Loss: 7.415e-02 Loss in Target Net: 3.157e-02 |
2020-01-31 21:59:20 Iteration 1700 Training Loss: 6.817e-02 Loss in Target Net: 2.583e-02 |
2020-01-31 21:59:44 Iteration 1750 Training Loss: 6.949e-02 Loss in Target Net: 2.457e-02 |
2020-01-31 22:00:09 Iteration 1800 Training Loss: 6.281e-02 Loss in Target Net: 2.977e-02 |
2020-01-31 22:00:33 Iteration 1850 Training Loss: 6.480e-02 Loss in Target Net: 2.221e-02 |
2020-01-31 22:00:55 Iteration 1900 Training Loss: 6.603e-02 Loss in Target Net: 2.306e-02 |
2020-01-31 22:01:19 Iteration 1950 Training Loss: 5.861e-02 Loss in Target Net: 2.398e-02 |
2020-01-31 22:01:42 Iteration 2000 Training Loss: 7.056e-02 Loss in Target Net: 3.131e-02 |
2020-01-31 22:02:05 Iteration 2050 Training Loss: 6.594e-02 Loss in Target Net: 2.891e-02 |
2020-01-31 22:02:28 Iteration 2100 Training Loss: 6.313e-02 Loss in Target Net: 2.515e-02 |
2020-01-31 22:02:51 Iteration 2150 Training Loss: 6.052e-02 Loss in Target Net: 2.478e-02 |
2020-01-31 22:03:14 Iteration 2200 Training Loss: 6.409e-02 Loss in Target Net: 2.344e-02 |
2020-01-31 22:03:36 Iteration 2250 Training Loss: 6.407e-02 Loss in Target Net: 2.227e-02 |
2020-01-31 22:04:00 Iteration 2300 Training Loss: 6.094e-02 Loss in Target Net: 2.460e-02 |
2020-01-31 22:04:24 Iteration 2350 Training Loss: 6.708e-02 Loss in Target Net: 2.678e-02 |
2020-01-31 22:04:46 Iteration 2400 Training Loss: 6.889e-02 Loss in Target Net: 2.117e-02 |
2020-01-31 22:05:08 Iteration 2450 Training Loss: 6.067e-02 Loss in Target Net: 1.877e-02 |
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