text stringlengths 5 1.13k |
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Files already downloaded and verified |
2020-01-31 18:10:10, Epoch 0, Iteration 7, loss 1.441 (3.719), acc 84.615 (65.000) |
2020-01-31 18:10:10, Epoch 30, Iteration 7, loss 0.206 (0.107), acc 96.154 (96.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-8.15619, -15.324083, -1.9174917, 7.6017847, -13.4786215, -8.944322, 15.458059, -26.467094, 9.279365, -34.10582], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 18:10:11 Epoch 59, Val iteration 0, acc 88.800 (88.800) |
2020-01-31 18:10:13 Epoch 59, Val iteration 19, acc 88.600 (87.180) |
* Prec: 87.18000221252441 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:10:15, Epoch 0, Iteration 7, loss 0.700 (0.801), acc 94.231 (85.000) |
2020-01-31 18:10:15, Epoch 30, Iteration 7, loss 0.018 (0.098), acc 100.000 (98.200) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-24.625755, -33.394146, -15.176044, 4.2175465, -31.531336, -0.069268316, 2.5754786, -34.761894, 6.7943788, -44.515503], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 18:10:16 Epoch 59, Val iteration 0, acc 93.000 (93.000) |
2020-01-31 18:10:18 Epoch 59, Val iteration 19, acc 93.800 (92.520) |
* Prec: 92.52000160217285 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 18:10:21, Epoch 0, Iteration 7, loss 0.681 (0.442), acc 90.385 (91.000) |
2020-01-31 18:10:21, Epoch 30, Iteration 7, loss 0.003 (0.003), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-4.044639, -12.12939, -8.202907, 2.0544155, -9.930147, -5.476587, 5.8668914, -24.614996, 5.0159535, -12.457275], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 18:10:23 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-01-31 18:10:27 Epoch 59, Val iteration 19, acc 93.800 (93.380) |
* Prec: 93.38000221252442 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 7 |
DPN92 1 |
SENet18 1 |
ResNet50 0 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
MobileNetV2 0 |
ResNet18 1 |
DenseNet121 0 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='0', 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=8, 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/4000/8 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:15:32 Iteration 0 Training Loss: 1.035e+00 Loss in Target Net: 3.841e-01 |
2020-01-31 18:15:55 Iteration 50 Training Loss: 1.172e-01 Loss in Target Net: 6.376e-03 |
2020-01-31 18:16:17 Iteration 100 Training Loss: 9.775e-02 Loss in Target Net: 4.893e-03 |
2020-01-31 18:16:40 Iteration 150 Training Loss: 9.281e-02 Loss in Target Net: 4.883e-03 |
2020-01-31 18:17:04 Iteration 200 Training Loss: 9.405e-02 Loss in Target Net: 6.209e-03 |
2020-01-31 18:17:26 Iteration 250 Training Loss: 9.325e-02 Loss in Target Net: 6.728e-03 |
2020-01-31 18:17:49 Iteration 300 Training Loss: 1.027e-01 Loss in Target Net: 5.071e-03 |
2020-01-31 18:18:10 Iteration 350 Training Loss: 8.793e-02 Loss in Target Net: 9.293e-03 |
2020-01-31 18:18:34 Iteration 400 Training Loss: 8.745e-02 Loss in Target Net: 6.208e-03 |
2020-01-31 18:18:58 Iteration 450 Training Loss: 9.384e-02 Loss in Target Net: 6.994e-03 |
2020-01-31 18:19:21 Iteration 500 Training Loss: 8.841e-02 Loss in Target Net: 5.209e-03 |
2020-01-31 18:19:44 Iteration 550 Training Loss: 9.012e-02 Loss in Target Net: 8.229e-03 |
2020-01-31 18:20:09 Iteration 600 Training Loss: 9.677e-02 Loss in Target Net: 6.755e-03 |
2020-01-31 18:20:34 Iteration 650 Training Loss: 9.770e-02 Loss in Target Net: 7.704e-03 |
2020-01-31 18:20:57 Iteration 700 Training Loss: 9.698e-02 Loss in Target Net: 6.423e-03 |
2020-01-31 18:21:20 Iteration 750 Training Loss: 9.343e-02 Loss in Target Net: 8.219e-03 |
2020-01-31 18:21:43 Iteration 800 Training Loss: 8.921e-02 Loss in Target Net: 6.959e-03 |
2020-01-31 18:22:05 Iteration 850 Training Loss: 9.130e-02 Loss in Target Net: 6.378e-03 |
2020-01-31 18:22:29 Iteration 900 Training Loss: 8.655e-02 Loss in Target Net: 1.156e-02 |
2020-01-31 18:22:51 Iteration 950 Training Loss: 9.085e-02 Loss in Target Net: 5.599e-03 |
2020-01-31 18:23:14 Iteration 1000 Training Loss: 8.500e-02 Loss in Target Net: 6.417e-03 |
2020-01-31 18:23:38 Iteration 1050 Training Loss: 8.610e-02 Loss in Target Net: 8.526e-03 |
2020-01-31 18:24:00 Iteration 1100 Training Loss: 9.162e-02 Loss in Target Net: 7.311e-03 |
2020-01-31 18:24:25 Iteration 1150 Training Loss: 9.796e-02 Loss in Target Net: 6.812e-03 |
2020-01-31 18:24:49 Iteration 1200 Training Loss: 8.470e-02 Loss in Target Net: 5.524e-03 |
2020-01-31 18:25:10 Iteration 1250 Training Loss: 9.377e-02 Loss in Target Net: 1.221e-02 |
2020-01-31 18:25:32 Iteration 1300 Training Loss: 8.636e-02 Loss in Target Net: 5.736e-03 |
2020-01-31 18:25:54 Iteration 1350 Training Loss: 8.820e-02 Loss in Target Net: 6.444e-03 |
2020-01-31 18:26:16 Iteration 1400 Training Loss: 8.436e-02 Loss in Target Net: 1.021e-02 |
2020-01-31 18:26:38 Iteration 1450 Training Loss: 8.668e-02 Loss in Target Net: 1.219e-02 |
2020-01-31 18:27:01 Iteration 1500 Training Loss: 9.007e-02 Loss in Target Net: 6.866e-03 |
2020-01-31 18:27:22 Iteration 1550 Training Loss: 8.559e-02 Loss in Target Net: 1.001e-02 |
2020-01-31 18:27:46 Iteration 1600 Training Loss: 8.919e-02 Loss in Target Net: 1.064e-02 |
2020-01-31 18:28:09 Iteration 1650 Training Loss: 8.863e-02 Loss in Target Net: 8.284e-03 |
2020-01-31 18:28:31 Iteration 1700 Training Loss: 8.496e-02 Loss in Target Net: 5.999e-03 |
2020-01-31 18:28:53 Iteration 1750 Training Loss: 8.993e-02 Loss in Target Net: 1.227e-02 |
2020-01-31 18:29:14 Iteration 1800 Training Loss: 9.145e-02 Loss in Target Net: 9.065e-03 |
2020-01-31 18:29:35 Iteration 1850 Training Loss: 8.977e-02 Loss in Target Net: 6.479e-03 |
2020-01-31 18:29:56 Iteration 1900 Training Loss: 8.174e-02 Loss in Target Net: 9.601e-03 |
2020-01-31 18:30:18 Iteration 1950 Training Loss: 8.838e-02 Loss in Target Net: 9.236e-03 |
2020-01-31 18:30:40 Iteration 2000 Training Loss: 9.462e-02 Loss in Target Net: 8.673e-03 |
2020-01-31 18:31:02 Iteration 2050 Training Loss: 9.036e-02 Loss in Target Net: 1.162e-02 |
2020-01-31 18:31:24 Iteration 2100 Training Loss: 8.882e-02 Loss in Target Net: 7.631e-03 |
2020-01-31 18:31:47 Iteration 2150 Training Loss: 8.682e-02 Loss in Target Net: 1.106e-02 |
2020-01-31 18:32:09 Iteration 2200 Training Loss: 8.314e-02 Loss in Target Net: 7.464e-03 |
2020-01-31 18:32:32 Iteration 2250 Training Loss: 8.869e-02 Loss in Target Net: 1.536e-02 |
2020-01-31 18:32:55 Iteration 2300 Training Loss: 8.673e-02 Loss in Target Net: 5.905e-03 |
2020-01-31 18:33:18 Iteration 2350 Training Loss: 8.781e-02 Loss in Target Net: 1.447e-02 |
2020-01-31 18:33:41 Iteration 2400 Training Loss: 8.483e-02 Loss in Target Net: 1.395e-02 |
2020-01-31 18:34:05 Iteration 2450 Training Loss: 8.336e-02 Loss in Target Net: 6.520e-03 |
2020-01-31 18:34:27 Iteration 2500 Training Loss: 9.199e-02 Loss in Target Net: 7.089e-03 |
2020-01-31 18:34:50 Iteration 2550 Training Loss: 8.574e-02 Loss in Target Net: 8.218e-03 |
2020-01-31 18:35:13 Iteration 2600 Training Loss: 9.185e-02 Loss in Target Net: 7.928e-03 |
2020-01-31 18:35:36 Iteration 2650 Training Loss: 9.119e-02 Loss in Target Net: 7.678e-03 |
2020-01-31 18:35:58 Iteration 2700 Training Loss: 9.306e-02 Loss in Target Net: 9.365e-03 |
2020-01-31 18:36:20 Iteration 2750 Training Loss: 9.337e-02 Loss in Target Net: 8.853e-03 |
2020-01-31 18:36:42 Iteration 2800 Training Loss: 8.551e-02 Loss in Target Net: 7.080e-03 |
2020-01-31 18:37:06 Iteration 2850 Training Loss: 8.630e-02 Loss in Target Net: 9.277e-03 |
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