text stringlengths 5 1.13k |
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GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:42:08, Epoch 0, Iteration 7, loss 0.601 (0.512), acc 84.615 (89.600) |
2020-01-31 19:42:09, Epoch 30, Iteration 7, loss 0.135 (0.078), acc 94.231 (97.800) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-40.116352, -11.9483185, -6.7562304, -3.2159963, -13.9315405, -6.560989, 12.6355295, -6.576988, 5.868739, -27.474262], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-01-31 19:42:11 Epoch 59, Val iteration 0, acc 92.200 (92.200) |
2020-01-31 19:42:16 Epoch 59, Val iteration 19, acc 90.400 (91.460) |
* Prec: 91.46000175476074 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:42:18, Epoch 0, Iteration 7, loss 1.532 (4.253), acc 78.846 (57.600) |
2020-01-31 19:42:18, Epoch 30, Iteration 7, loss 0.316 (0.236), acc 92.308 (93.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[2.4120114, 3.3360636, 5.510596, 17.206764, -2.5637898, 1.2243775, 28.369495, -24.984186, 21.95895, -12.943037], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 19:42:19 Epoch 59, Val iteration 0, acc 88.000 (88.000) |
2020-01-31 19:42:21 Epoch 59, Val iteration 19, acc 87.800 (86.500) |
* Prec: 86.50000114440918 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:42:23, Epoch 0, Iteration 7, loss 0.109 (0.532), acc 96.154 (87.600) |
2020-01-31 19:42:23, Epoch 30, Iteration 7, loss 0.027 (0.055), acc 98.077 (98.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-34.98214, -9.795134, -13.043227, 2.468245, -42.22657, -12.31922, 11.16998, -17.68488, 9.6927595, -45.609074], Poisons' Predictions:[6, 8, 6, 8, 8] |
2020-01-31 19:42:24 Epoch 59, Val iteration 0, acc 93.600 (93.600) |
2020-01-31 19:42:26 Epoch 59, Val iteration 19, acc 94.000 (92.470) |
* Prec: 92.47000122070312 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:42:29, Epoch 0, Iteration 7, loss 0.537 (0.428), acc 84.615 (92.200) |
2020-01-31 19:42:29, Epoch 30, Iteration 7, loss 0.005 (0.008), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-13.006287, -23.2254, -19.529068, -7.985793, -9.777575, -9.343292, 2.57767, -36.85769, 2.1322155, -19.59308], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 19:42:31 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-01-31 19:42:35 Epoch 59, Val iteration 19, acc 93.200 (93.070) |
* Prec: 93.07000122070312 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 28 |
TARGET INDEX: 17 |
DPN92 0 |
SENet18 0 |
ResNet50 0 |
ResNeXt29_2x64d 0 |
GoogLeNet 0 |
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='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=18, 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/4000/18 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 19:12:11 Iteration 0 Training Loss: 1.122e+00 Loss in Target Net: 4.208e-01 |
2020-01-31 19:12:32 Iteration 50 Training Loss: 1.154e-01 Loss in Target Net: 1.950e-02 |
2020-01-31 19:12:56 Iteration 100 Training Loss: 1.019e-01 Loss in Target Net: 1.494e-02 |
2020-01-31 19:13:20 Iteration 150 Training Loss: 1.006e-01 Loss in Target Net: 1.113e-02 |
2020-01-31 19:13:45 Iteration 200 Training Loss: 9.219e-02 Loss in Target Net: 6.463e-03 |
2020-01-31 19:14:09 Iteration 250 Training Loss: 9.664e-02 Loss in Target Net: 5.830e-03 |
2020-01-31 19:14:34 Iteration 300 Training Loss: 9.039e-02 Loss in Target Net: 6.507e-03 |
2020-01-31 19:14:58 Iteration 350 Training Loss: 8.700e-02 Loss in Target Net: 9.336e-03 |
2020-01-31 19:15:23 Iteration 400 Training Loss: 8.767e-02 Loss in Target Net: 1.049e-02 |
2020-01-31 19:15:48 Iteration 450 Training Loss: 8.707e-02 Loss in Target Net: 1.079e-02 |
2020-01-31 19:16:11 Iteration 500 Training Loss: 8.488e-02 Loss in Target Net: 9.184e-03 |
2020-01-31 19:16:34 Iteration 550 Training Loss: 8.193e-02 Loss in Target Net: 1.088e-02 |
2020-01-31 19:16:56 Iteration 600 Training Loss: 8.352e-02 Loss in Target Net: 9.301e-03 |
2020-01-31 19:17:18 Iteration 650 Training Loss: 8.265e-02 Loss in Target Net: 1.150e-02 |
2020-01-31 19:17:40 Iteration 700 Training Loss: 8.708e-02 Loss in Target Net: 9.538e-03 |
2020-01-31 19:18:02 Iteration 750 Training Loss: 8.321e-02 Loss in Target Net: 1.316e-02 |
2020-01-31 19:18:26 Iteration 800 Training Loss: 8.489e-02 Loss in Target Net: 1.144e-02 |
2020-01-31 19:18:51 Iteration 850 Training Loss: 8.831e-02 Loss in Target Net: 8.166e-03 |
2020-01-31 19:19:14 Iteration 900 Training Loss: 8.560e-02 Loss in Target Net: 9.145e-03 |
2020-01-31 19:19:39 Iteration 950 Training Loss: 7.635e-02 Loss in Target Net: 8.314e-03 |
2020-01-31 19:20:04 Iteration 1000 Training Loss: 7.823e-02 Loss in Target Net: 7.351e-03 |
2020-01-31 19:20:28 Iteration 1050 Training Loss: 7.920e-02 Loss in Target Net: 7.747e-03 |
2020-01-31 19:20:53 Iteration 1100 Training Loss: 7.803e-02 Loss in Target Net: 1.008e-02 |
2020-01-31 19:21:17 Iteration 1150 Training Loss: 8.617e-02 Loss in Target Net: 1.085e-02 |
2020-01-31 19:21:42 Iteration 1200 Training Loss: 8.202e-02 Loss in Target Net: 1.139e-02 |
2020-01-31 19:22:06 Iteration 1250 Training Loss: 8.071e-02 Loss in Target Net: 9.794e-03 |
2020-01-31 19:22:31 Iteration 1300 Training Loss: 7.956e-02 Loss in Target Net: 1.280e-02 |
2020-01-31 19:22:53 Iteration 1350 Training Loss: 8.374e-02 Loss in Target Net: 1.380e-02 |
2020-01-31 19:23:16 Iteration 1400 Training Loss: 8.445e-02 Loss in Target Net: 9.772e-03 |
2020-01-31 19:23:38 Iteration 1450 Training Loss: 8.004e-02 Loss in Target Net: 1.010e-02 |
2020-01-31 19:24:01 Iteration 1500 Training Loss: 7.905e-02 Loss in Target Net: 1.502e-02 |
2020-01-31 19:24:22 Iteration 1550 Training Loss: 7.566e-02 Loss in Target Net: 9.804e-03 |
2020-01-31 19:24:45 Iteration 1600 Training Loss: 7.733e-02 Loss in Target Net: 1.263e-02 |
2020-01-31 19:25:06 Iteration 1650 Training Loss: 7.711e-02 Loss in Target Net: 8.329e-03 |
2020-01-31 19:25:29 Iteration 1700 Training Loss: 8.108e-02 Loss in Target Net: 1.614e-02 |
2020-01-31 19:25:51 Iteration 1750 Training Loss: 7.328e-02 Loss in Target Net: 1.260e-02 |
2020-01-31 19:26:15 Iteration 1800 Training Loss: 7.808e-02 Loss in Target Net: 1.161e-02 |
2020-01-31 19:26:40 Iteration 1850 Training Loss: 7.536e-02 Loss in Target Net: 9.658e-03 |
2020-01-31 19:27:04 Iteration 1900 Training Loss: 7.160e-02 Loss in Target Net: 9.668e-03 |
2020-01-31 19:27:28 Iteration 1950 Training Loss: 8.034e-02 Loss in Target Net: 1.163e-02 |
2020-01-31 19:27:53 Iteration 2000 Training Loss: 8.076e-02 Loss in Target Net: 1.117e-02 |
2020-01-31 19:28:17 Iteration 2050 Training Loss: 8.374e-02 Loss in Target Net: 1.499e-02 |
2020-01-31 19:28:42 Iteration 2100 Training Loss: 8.102e-02 Loss in Target Net: 1.318e-02 |
2020-01-31 19:29:06 Iteration 2150 Training Loss: 7.778e-02 Loss in Target Net: 1.611e-02 |
2020-01-31 19:29:28 Iteration 2200 Training Loss: 8.282e-02 Loss in Target Net: 1.029e-02 |
2020-01-31 19:29:50 Iteration 2250 Training Loss: 8.149e-02 Loss in Target Net: 1.138e-02 |
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