text stringlengths 5 1.13k |
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2020-01-31 19:11:56, Epoch 30, Iteration 7, loss 0.018 (0.036), acc 100.000 (98.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-28.664742, -7.8661437, -15.626997, -2.3101084, -13.786006, -7.9602065, 11.00368, -6.7034526, 11.993952, -21.639519], Poisons' Predictions:[8, 6, 8, 8, 8] |
2020-01-31 19:11:58 Epoch 59, Val iteration 0, acc 90.800 (90.800) |
2020-01-31 19:12:03 Epoch 59, Val iteration 19, acc 90.800 (91.860) |
* Prec: 91.86000137329101 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:12:05, Epoch 0, Iteration 7, loss 1.486 (2.722), acc 82.692 (68.200) |
2020-01-31 19:12:06, Epoch 30, Iteration 7, loss 0.401 (0.311), acc 88.462 (92.600) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-3.7441812, -26.623022, -2.568223, 9.359434, -25.910002, -8.994488, 19.092045, -42.54108, 19.569244, -30.79679], Poisons' Predictions:[8, 8, 6, 6, 8] |
2020-01-31 19:12:06 Epoch 59, Val iteration 0, acc 87.800 (87.800) |
2020-01-31 19:12:08 Epoch 59, Val iteration 19, acc 88.600 (86.770) |
* Prec: 86.77000160217285 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:12:10, Epoch 0, Iteration 7, loss 1.520 (0.608), acc 88.462 (87.400) |
2020-01-31 19:12:11, Epoch 30, Iteration 7, loss 0.018 (0.031), acc 100.000 (99.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-34.612095, -6.4116683, -18.179634, 0.6107256, -36.201805, -11.786036, 11.434129, -24.526201, 8.900637, -27.400328], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-01-31 19:12:11 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-01-31 19:12:13 Epoch 59, Val iteration 19, acc 93.000 (92.500) |
* Prec: 92.50000076293945 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:12:16, Epoch 0, Iteration 7, loss 0.412 (0.377), acc 94.231 (93.200) |
2020-01-31 19:12:16, Epoch 30, Iteration 7, loss 0.001 (0.003), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-8.523682, -11.301686, -6.97476, -3.0678883, -1.8557749, -3.9529102, 7.35726, -33.05529, 5.463373, -14.587259], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 19:12:18 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-01-31 19:12:22 Epoch 59, Val iteration 19, acc 92.600 (92.830) |
* Prec: 92.83000144958496 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 13 |
DPN92 0 |
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='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=14, 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/4000/14 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:41:53 Iteration 0 Training Loss: 1.210e+00 Loss in Target Net: 5.058e-01 |
2020-01-31 18:42:14 Iteration 50 Training Loss: 1.222e-01 Loss in Target Net: 5.578e-02 |
2020-01-31 18:42:35 Iteration 100 Training Loss: 1.150e-01 Loss in Target Net: 6.008e-02 |
2020-01-31 18:42:56 Iteration 150 Training Loss: 1.139e-01 Loss in Target Net: 6.789e-02 |
2020-01-31 18:43:16 Iteration 200 Training Loss: 1.036e-01 Loss in Target Net: 2.735e-02 |
2020-01-31 18:43:37 Iteration 250 Training Loss: 9.444e-02 Loss in Target Net: 4.700e-02 |
2020-01-31 18:43:57 Iteration 300 Training Loss: 1.080e-01 Loss in Target Net: 2.149e-02 |
2020-01-31 18:44:18 Iteration 350 Training Loss: 9.207e-02 Loss in Target Net: 5.599e-02 |
2020-01-31 18:44:41 Iteration 400 Training Loss: 1.012e-01 Loss in Target Net: 4.267e-02 |
2020-01-31 18:45:01 Iteration 450 Training Loss: 9.432e-02 Loss in Target Net: 3.763e-02 |
2020-01-31 18:45:23 Iteration 500 Training Loss: 9.832e-02 Loss in Target Net: 2.021e-02 |
2020-01-31 18:45:45 Iteration 550 Training Loss: 1.008e-01 Loss in Target Net: 4.718e-02 |
2020-01-31 18:46:06 Iteration 600 Training Loss: 1.008e-01 Loss in Target Net: 5.258e-02 |
2020-01-31 18:46:27 Iteration 650 Training Loss: 9.452e-02 Loss in Target Net: 6.876e-02 |
2020-01-31 18:46:49 Iteration 700 Training Loss: 9.771e-02 Loss in Target Net: 3.874e-02 |
2020-01-31 18:47:10 Iteration 750 Training Loss: 1.007e-01 Loss in Target Net: 6.735e-02 |
2020-01-31 18:47:31 Iteration 800 Training Loss: 1.005e-01 Loss in Target Net: 5.335e-02 |
2020-01-31 18:47:51 Iteration 850 Training Loss: 9.446e-02 Loss in Target Net: 5.474e-02 |
2020-01-31 18:48:11 Iteration 900 Training Loss: 9.726e-02 Loss in Target Net: 5.741e-02 |
2020-01-31 18:48:32 Iteration 950 Training Loss: 9.447e-02 Loss in Target Net: 3.569e-02 |
2020-01-31 18:48:52 Iteration 1000 Training Loss: 9.611e-02 Loss in Target Net: 4.300e-02 |
2020-01-31 18:49:13 Iteration 1050 Training Loss: 8.919e-02 Loss in Target Net: 6.377e-02 |
2020-01-31 18:49:34 Iteration 1100 Training Loss: 1.024e-01 Loss in Target Net: 5.978e-02 |
2020-01-31 18:49:56 Iteration 1150 Training Loss: 8.392e-02 Loss in Target Net: 4.833e-02 |
2020-01-31 18:50:17 Iteration 1200 Training Loss: 9.145e-02 Loss in Target Net: 6.571e-02 |
2020-01-31 18:50:38 Iteration 1250 Training Loss: 9.632e-02 Loss in Target Net: 5.402e-02 |
2020-01-31 18:50:59 Iteration 1300 Training Loss: 9.915e-02 Loss in Target Net: 5.244e-02 |
2020-01-31 18:51:21 Iteration 1350 Training Loss: 9.712e-02 Loss in Target Net: 5.296e-02 |
2020-01-31 18:51:41 Iteration 1400 Training Loss: 8.959e-02 Loss in Target Net: 4.620e-02 |
2020-01-31 18:52:02 Iteration 1450 Training Loss: 9.798e-02 Loss in Target Net: 2.943e-02 |
2020-01-31 18:52:23 Iteration 1500 Training Loss: 1.005e-01 Loss in Target Net: 6.610e-02 |
2020-01-31 18:52:46 Iteration 1550 Training Loss: 9.497e-02 Loss in Target Net: 5.709e-02 |
2020-01-31 18:53:07 Iteration 1600 Training Loss: 9.033e-02 Loss in Target Net: 4.295e-02 |
2020-01-31 18:53:29 Iteration 1650 Training Loss: 9.938e-02 Loss in Target Net: 4.304e-02 |
2020-01-31 18:53:51 Iteration 1700 Training Loss: 9.715e-02 Loss in Target Net: 5.563e-02 |
2020-01-31 18:54:15 Iteration 1750 Training Loss: 9.581e-02 Loss in Target Net: 6.316e-02 |
2020-01-31 18:54:37 Iteration 1800 Training Loss: 9.311e-02 Loss in Target Net: 6.060e-02 |
2020-01-31 18:54:58 Iteration 1850 Training Loss: 9.192e-02 Loss in Target Net: 5.782e-02 |
2020-01-31 18:55:20 Iteration 1900 Training Loss: 9.195e-02 Loss in Target Net: 6.535e-02 |
2020-01-31 18:55:42 Iteration 1950 Training Loss: 9.298e-02 Loss in Target Net: 5.427e-02 |
2020-01-31 18:56:04 Iteration 2000 Training Loss: 9.203e-02 Loss in Target Net: 6.560e-02 |
2020-01-31 18:56:25 Iteration 2050 Training Loss: 9.210e-02 Loss in Target Net: 6.124e-02 |
2020-01-31 18:56:46 Iteration 2100 Training Loss: 9.061e-02 Loss in Target Net: 8.539e-02 |
2020-01-31 18:57:07 Iteration 2150 Training Loss: 8.973e-02 Loss in Target Net: 5.044e-02 |
2020-01-31 18:57:27 Iteration 2200 Training Loss: 9.564e-02 Loss in Target Net: 6.213e-02 |
2020-01-31 18:57:48 Iteration 2250 Training Loss: 9.894e-02 Loss in Target Net: 7.263e-02 |
2020-01-31 18:58:08 Iteration 2300 Training Loss: 9.207e-02 Loss in Target Net: 7.803e-02 |
2020-01-31 18:58:30 Iteration 2350 Training Loss: 9.067e-02 Loss in Target Net: 8.247e-02 |
2020-01-31 18:58:51 Iteration 2400 Training Loss: 9.381e-02 Loss in Target Net: 7.705e-02 |
2020-01-31 18:59:12 Iteration 2450 Training Loss: 9.134e-02 Loss in Target Net: 6.295e-02 |
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