text stringlengths 5 1.13k |
|---|
* Prec: 91.49000244140625 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:40:59, Epoch 0, Iteration 7, loss 0.882 (3.491), acc 80.769 (58.400) |
2020-01-31 17:41:00, Epoch 30, Iteration 7, loss 0.134 (0.197), acc 96.154 (93.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[3.693559, 9.08897, -4.517484, 10.422432, -23.44697, -3.2152138, 16.8538, -37.96225, 16.679976, 4.6956124], Poisons' Predictions:[6, 8, 8, 6, 8] |
2020-01-31 17:41:00 Epoch 59, Val iteration 0, acc 87.800 (87.800) |
2020-01-31 17:41:03 Epoch 59, Val iteration 19, acc 87.400 (86.920) |
* Prec: 86.92000122070313 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:41:04, Epoch 0, Iteration 7, loss 0.884 (0.708), acc 88.462 (86.400) |
2020-01-31 17:41:05, Epoch 30, Iteration 7, loss 0.027 (0.020), acc 98.077 (99.200) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-38.597977, -8.900702, -23.651136, 0.43816036, -42.17196, -19.305593, 2.3484192, -14.80463, 8.292887, -31.327349], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 17:41:05 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 17:41:07 Epoch 59, Val iteration 19, acc 93.600 (92.720) |
* Prec: 92.72000198364258 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:41:10, Epoch 0, Iteration 7, loss 0.671 (0.424), acc 92.308 (91.200) |
2020-01-31 17:41:10, Epoch 30, Iteration 7, loss 0.001 (0.006), acc 100.000 (99.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-13.826047, -13.866153, -19.799444, -4.155402, -8.159716, -9.39407, 4.27906, -29.98489, 5.0974503, -15.822291], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 17:41:12 Epoch 59, Val iteration 0, acc 94.200 (94.200) |
2020-01-31 17:41:16 Epoch 59, Val iteration 19, acc 93.400 (93.150) |
* Prec: 93.1500015258789 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 1 |
DPN92 1 |
SENet18 1 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
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='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=10, 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/4000/10 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:12:28 Iteration 0 Training Loss: 1.084e+00 Loss in Target Net: 3.384e-01 |
2020-01-31 18:12:48 Iteration 50 Training Loss: 8.615e-02 Loss in Target Net: 6.121e-03 |
2020-01-31 18:13:08 Iteration 100 Training Loss: 7.928e-02 Loss in Target Net: 1.044e-02 |
2020-01-31 18:13:29 Iteration 150 Training Loss: 7.639e-02 Loss in Target Net: 1.124e-02 |
2020-01-31 18:13:49 Iteration 200 Training Loss: 7.106e-02 Loss in Target Net: 5.648e-03 |
2020-01-31 18:14:09 Iteration 250 Training Loss: 7.335e-02 Loss in Target Net: 1.150e-02 |
2020-01-31 18:14:29 Iteration 300 Training Loss: 7.375e-02 Loss in Target Net: 1.026e-02 |
2020-01-31 18:14:50 Iteration 350 Training Loss: 7.022e-02 Loss in Target Net: 7.741e-03 |
2020-01-31 18:15:12 Iteration 400 Training Loss: 7.367e-02 Loss in Target Net: 9.207e-03 |
2020-01-31 18:15:33 Iteration 450 Training Loss: 6.959e-02 Loss in Target Net: 6.638e-03 |
2020-01-31 18:15:54 Iteration 500 Training Loss: 6.550e-02 Loss in Target Net: 8.046e-03 |
2020-01-31 18:16:17 Iteration 550 Training Loss: 6.002e-02 Loss in Target Net: 3.910e-03 |
2020-01-31 18:16:39 Iteration 600 Training Loss: 6.198e-02 Loss in Target Net: 1.046e-02 |
2020-01-31 18:17:00 Iteration 650 Training Loss: 6.312e-02 Loss in Target Net: 9.097e-03 |
2020-01-31 18:17:23 Iteration 700 Training Loss: 6.665e-02 Loss in Target Net: 6.673e-03 |
2020-01-31 18:17:45 Iteration 750 Training Loss: 6.524e-02 Loss in Target Net: 6.442e-03 |
2020-01-31 18:18:07 Iteration 800 Training Loss: 6.599e-02 Loss in Target Net: 9.005e-03 |
2020-01-31 18:18:28 Iteration 850 Training Loss: 7.113e-02 Loss in Target Net: 1.136e-02 |
2020-01-31 18:18:49 Iteration 900 Training Loss: 6.806e-02 Loss in Target Net: 7.266e-03 |
2020-01-31 18:19:10 Iteration 950 Training Loss: 6.363e-02 Loss in Target Net: 8.281e-03 |
2020-01-31 18:19:30 Iteration 1000 Training Loss: 7.478e-02 Loss in Target Net: 9.658e-03 |
2020-01-31 18:19:52 Iteration 1050 Training Loss: 6.449e-02 Loss in Target Net: 1.054e-02 |
2020-01-31 18:20:13 Iteration 1100 Training Loss: 6.340e-02 Loss in Target Net: 1.311e-02 |
2020-01-31 18:20:33 Iteration 1150 Training Loss: 7.102e-02 Loss in Target Net: 8.877e-03 |
2020-01-31 18:20:54 Iteration 1200 Training Loss: 6.428e-02 Loss in Target Net: 7.641e-03 |
2020-01-31 18:21:16 Iteration 1250 Training Loss: 7.306e-02 Loss in Target Net: 1.206e-02 |
2020-01-31 18:21:39 Iteration 1300 Training Loss: 6.341e-02 Loss in Target Net: 6.601e-03 |
2020-01-31 18:22:00 Iteration 1350 Training Loss: 6.018e-02 Loss in Target Net: 7.756e-03 |
2020-01-31 18:22:21 Iteration 1400 Training Loss: 6.554e-02 Loss in Target Net: 8.329e-03 |
2020-01-31 18:22:42 Iteration 1450 Training Loss: 6.578e-02 Loss in Target Net: 9.504e-03 |
2020-01-31 18:23:03 Iteration 1500 Training Loss: 6.713e-02 Loss in Target Net: 6.041e-03 |
2020-01-31 18:23:24 Iteration 1550 Training Loss: 6.471e-02 Loss in Target Net: 7.492e-03 |
2020-01-31 18:23:45 Iteration 1600 Training Loss: 6.672e-02 Loss in Target Net: 5.075e-03 |
2020-01-31 18:24:07 Iteration 1650 Training Loss: 6.473e-02 Loss in Target Net: 5.804e-03 |
2020-01-31 18:24:28 Iteration 1700 Training Loss: 6.501e-02 Loss in Target Net: 6.232e-03 |
2020-01-31 18:24:49 Iteration 1750 Training Loss: 6.631e-02 Loss in Target Net: 9.981e-03 |
2020-01-31 18:25:10 Iteration 1800 Training Loss: 6.408e-02 Loss in Target Net: 9.640e-03 |
2020-01-31 18:25:32 Iteration 1850 Training Loss: 6.556e-02 Loss in Target Net: 8.891e-03 |
2020-01-31 18:25:53 Iteration 1900 Training Loss: 6.063e-02 Loss in Target Net: 1.078e-02 |
2020-01-31 18:26:14 Iteration 1950 Training Loss: 7.028e-02 Loss in Target Net: 8.721e-03 |
2020-01-31 18:26:37 Iteration 2000 Training Loss: 6.721e-02 Loss in Target Net: 7.099e-03 |
2020-01-31 18:26:58 Iteration 2050 Training Loss: 6.921e-02 Loss in Target Net: 4.806e-03 |
2020-01-31 18:27:18 Iteration 2100 Training Loss: 7.323e-02 Loss in Target Net: 1.215e-02 |
2020-01-31 18:27:39 Iteration 2150 Training Loss: 7.371e-02 Loss in Target Net: 7.693e-03 |
2020-01-31 18:27:59 Iteration 2200 Training Loss: 6.755e-02 Loss in Target Net: 1.185e-02 |
2020-01-31 18:28:21 Iteration 2250 Training Loss: 6.652e-02 Loss in Target Net: 1.172e-02 |
2020-01-31 18:28:41 Iteration 2300 Training Loss: 6.182e-02 Loss in Target Net: 7.757e-03 |
2020-01-31 18:29:04 Iteration 2350 Training Loss: 5.930e-02 Loss in Target Net: 5.213e-03 |
2020-01-31 18:29:27 Iteration 2400 Training Loss: 6.630e-02 Loss in Target Net: 5.247e-03 |
2020-01-31 18:29:49 Iteration 2450 Training Loss: 5.963e-02 Loss in Target Net: 9.339e-03 |
2020-01-31 18:30:10 Iteration 2500 Training Loss: 6.029e-02 Loss in Target Net: 5.272e-03 |
2020-01-31 18:30:32 Iteration 2550 Training Loss: 6.803e-02 Loss in Target Net: 1.241e-02 |
2020-01-31 18:30:54 Iteration 2600 Training Loss: 6.261e-02 Loss in Target Net: 7.044e-03 |
2020-01-31 18:31:15 Iteration 2650 Training Loss: 6.414e-02 Loss in Target Net: 6.466e-03 |
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