text stringlengths 5 1.13k |
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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='10', 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=42, 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/4000/42 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 21:22:18 Iteration 0 Training Loss: 1.115e+00 Loss in Target Net: 4.673e-01 |
2020-02-04 21:23:36 Iteration 50 Training Loss: 9.496e-02 Loss in Target Net: 8.052e-03 |
2020-02-04 21:24:54 Iteration 100 Training Loss: 8.049e-02 Loss in Target Net: 9.600e-03 |
2020-02-04 21:26:12 Iteration 150 Training Loss: 7.743e-02 Loss in Target Net: 8.047e-03 |
2020-02-04 21:27:32 Iteration 200 Training Loss: 7.847e-02 Loss in Target Net: 9.467e-03 |
2020-02-04 21:28:53 Iteration 250 Training Loss: 7.827e-02 Loss in Target Net: 1.092e-02 |
2020-02-04 21:30:12 Iteration 300 Training Loss: 6.954e-02 Loss in Target Net: 9.207e-03 |
2020-02-04 21:31:30 Iteration 350 Training Loss: 7.520e-02 Loss in Target Net: 8.926e-03 |
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2020-02-04 21:34:08 Iteration 450 Training Loss: 7.144e-02 Loss in Target Net: 7.512e-03 |
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2020-02-04 21:36:46 Iteration 550 Training Loss: 6.928e-02 Loss in Target Net: 9.127e-03 |
2020-02-04 21:38:05 Iteration 600 Training Loss: 7.368e-02 Loss in Target Net: 9.045e-03 |
2020-02-04 21:39:24 Iteration 650 Training Loss: 7.125e-02 Loss in Target Net: 5.077e-03 |
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2020-02-04 22:21:50 Iteration 2050 Training Loss: 6.806e-02 Loss in Target Net: 8.185e-03 |
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2020-02-04 22:34:25 Iteration 2450 Training Loss: 7.041e-02 Loss in Target Net: 5.600e-03 |
2020-02-04 22:36:00 Iteration 2500 Training Loss: 6.850e-02 Loss in Target Net: 7.273e-03 |
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2020-02-04 22:39:08 Iteration 2600 Training Loss: 6.878e-02 Loss in Target Net: 8.833e-03 |
2020-02-04 22:40:42 Iteration 2650 Training Loss: 6.952e-02 Loss in Target Net: 8.134e-03 |
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2020-02-04 22:46:35 Iteration 2850 Training Loss: 6.673e-02 Loss in Target Net: 6.507e-03 |
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2020-02-04 22:57:24 Iteration 3200 Training Loss: 7.217e-02 Loss in Target Net: 6.503e-03 |
2020-02-04 22:58:59 Iteration 3250 Training Loss: 7.529e-02 Loss in Target Net: 7.511e-03 |
2020-02-04 23:00:32 Iteration 3300 Training Loss: 7.659e-02 Loss in Target Net: 5.819e-03 |
2020-02-04 23:02:06 Iteration 3350 Training Loss: 6.540e-02 Loss in Target Net: 5.976e-03 |
2020-02-04 23:03:40 Iteration 3400 Training Loss: 6.978e-02 Loss in Target Net: 8.375e-03 |
2020-02-04 23:05:12 Iteration 3450 Training Loss: 6.463e-02 Loss in Target Net: 8.894e-03 |
2020-02-04 23:06:43 Iteration 3500 Training Loss: 6.965e-02 Loss in Target Net: 7.763e-03 |
2020-02-04 23:08:12 Iteration 3550 Training Loss: 6.951e-02 Loss in Target Net: 9.625e-03 |
2020-02-04 23:09:43 Iteration 3600 Training Loss: 7.470e-02 Loss in Target Net: 7.131e-03 |
2020-02-04 23:11:13 Iteration 3650 Training Loss: 7.338e-02 Loss in Target Net: 6.754e-03 |
2020-02-04 23:12:39 Iteration 3700 Training Loss: 7.011e-02 Loss in Target Net: 6.627e-03 |
2020-02-04 23:14:02 Iteration 3750 Training Loss: 7.339e-02 Loss in Target Net: 6.010e-03 |
2020-02-04 23:15:25 Iteration 3800 Training Loss: 6.638e-02 Loss in Target Net: 7.442e-03 |
2020-02-04 23:16:47 Iteration 3850 Training Loss: 6.880e-02 Loss in Target Net: 4.757e-03 |
2020-02-04 23:18:08 Iteration 3900 Training Loss: 6.566e-02 Loss in Target Net: 8.955e-03 |
2020-02-04 23:19:26 Iteration 3950 Training Loss: 7.048e-02 Loss in Target Net: 6.727e-03 |
2020-02-04 23:20:43 Iteration 3999 Training Loss: 7.020e-02 Loss in Target Net: 5.712e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:21:04, Epoch 0, Iteration 7, loss 1.079 (4.971), acc 84.615 (62.800) |
2020-02-04 23:21:04, Epoch 30, Iteration 7, loss 0.133 (0.116), acc 98.077 (96.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[19.51406, -16.91381, -49.090424, 2.6831965, -24.519444, -7.4737053, 32.677906, -39.6215, 31.72906, -72.34801], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-02-04 23:21:38 Epoch 59, Val iteration 0, acc 91.000 (91.000) |
2020-02-04 23:22:26 Epoch 59, Val iteration 19, acc 92.000 (92.190) |
* Prec: 92.1900016784668 |
-------- |
SENet18 |
Using Adam for retraining |
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