text stringlengths 5 1.13k |
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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='3', 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=23, 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/4000/23 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 19:41:25 Iteration 0 Training Loss: 1.061e+00 Loss in Target Net: 3.561e-01 |
2020-01-31 19:41:47 Iteration 50 Training Loss: 8.153e-02 Loss in Target Net: 3.975e-03 |
2020-01-31 19:42:09 Iteration 100 Training Loss: 6.903e-02 Loss in Target Net: 2.582e-03 |
2020-01-31 19:42:31 Iteration 150 Training Loss: 6.795e-02 Loss in Target Net: 4.063e-03 |
2020-01-31 19:42:52 Iteration 200 Training Loss: 6.524e-02 Loss in Target Net: 4.003e-03 |
2020-01-31 19:43:14 Iteration 250 Training Loss: 6.679e-02 Loss in Target Net: 4.530e-03 |
2020-01-31 19:43:35 Iteration 300 Training Loss: 5.815e-02 Loss in Target Net: 4.106e-03 |
2020-01-31 19:43:56 Iteration 350 Training Loss: 6.585e-02 Loss in Target Net: 3.117e-03 |
2020-01-31 19:44:17 Iteration 400 Training Loss: 6.265e-02 Loss in Target Net: 3.875e-03 |
2020-01-31 19:44:38 Iteration 450 Training Loss: 6.464e-02 Loss in Target Net: 4.172e-03 |
2020-01-31 19:45:00 Iteration 500 Training Loss: 6.711e-02 Loss in Target Net: 2.482e-03 |
2020-01-31 19:45:21 Iteration 550 Training Loss: 5.829e-02 Loss in Target Net: 3.369e-03 |
2020-01-31 19:45:43 Iteration 600 Training Loss: 6.126e-02 Loss in Target Net: 3.687e-03 |
2020-01-31 19:46:04 Iteration 650 Training Loss: 6.378e-02 Loss in Target Net: 3.042e-03 |
2020-01-31 19:46:25 Iteration 700 Training Loss: 6.057e-02 Loss in Target Net: 1.858e-03 |
2020-01-31 19:46:47 Iteration 750 Training Loss: 6.877e-02 Loss in Target Net: 3.041e-03 |
2020-01-31 19:47:08 Iteration 800 Training Loss: 6.292e-02 Loss in Target Net: 3.252e-03 |
2020-01-31 19:47:29 Iteration 850 Training Loss: 6.600e-02 Loss in Target Net: 2.698e-03 |
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2020-01-31 19:48:12 Iteration 950 Training Loss: 6.378e-02 Loss in Target Net: 1.800e-03 |
2020-01-31 19:48:33 Iteration 1000 Training Loss: 5.908e-02 Loss in Target Net: 2.408e-03 |
2020-01-31 19:48:54 Iteration 1050 Training Loss: 6.240e-02 Loss in Target Net: 3.336e-03 |
2020-01-31 19:49:16 Iteration 1100 Training Loss: 6.110e-02 Loss in Target Net: 5.061e-03 |
2020-01-31 19:49:37 Iteration 1150 Training Loss: 6.851e-02 Loss in Target Net: 5.179e-03 |
2020-01-31 19:49:59 Iteration 1200 Training Loss: 6.238e-02 Loss in Target Net: 2.506e-03 |
2020-01-31 19:50:20 Iteration 1250 Training Loss: 5.800e-02 Loss in Target Net: 4.285e-03 |
2020-01-31 19:50:41 Iteration 1300 Training Loss: 6.735e-02 Loss in Target Net: 3.058e-03 |
2020-01-31 19:51:02 Iteration 1350 Training Loss: 6.295e-02 Loss in Target Net: 5.021e-03 |
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2020-01-31 19:51:45 Iteration 1450 Training Loss: 6.220e-02 Loss in Target Net: 3.379e-03 |
2020-01-31 19:52:07 Iteration 1500 Training Loss: 5.928e-02 Loss in Target Net: 4.959e-03 |
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2020-01-31 19:52:50 Iteration 1600 Training Loss: 6.640e-02 Loss in Target Net: 4.797e-03 |
2020-01-31 19:53:12 Iteration 1650 Training Loss: 6.667e-02 Loss in Target Net: 6.688e-03 |
2020-01-31 19:53:33 Iteration 1700 Training Loss: 6.104e-02 Loss in Target Net: 7.897e-03 |
2020-01-31 19:53:55 Iteration 1750 Training Loss: 6.513e-02 Loss in Target Net: 4.793e-03 |
2020-01-31 19:54:16 Iteration 1800 Training Loss: 6.058e-02 Loss in Target Net: 4.589e-03 |
2020-01-31 19:54:37 Iteration 1850 Training Loss: 6.246e-02 Loss in Target Net: 2.682e-03 |
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2020-01-31 19:55:41 Iteration 2000 Training Loss: 6.194e-02 Loss in Target Net: 3.550e-03 |
2020-01-31 19:56:03 Iteration 2050 Training Loss: 5.762e-02 Loss in Target Net: 3.750e-03 |
2020-01-31 19:56:24 Iteration 2100 Training Loss: 6.470e-02 Loss in Target Net: 3.161e-03 |
2020-01-31 19:56:45 Iteration 2150 Training Loss: 6.098e-02 Loss in Target Net: 4.462e-03 |
2020-01-31 19:57:06 Iteration 2200 Training Loss: 5.851e-02 Loss in Target Net: 5.450e-03 |
2020-01-31 19:57:27 Iteration 2250 Training Loss: 6.350e-02 Loss in Target Net: 4.452e-03 |
2020-01-31 19:57:48 Iteration 2300 Training Loss: 5.955e-02 Loss in Target Net: 6.099e-03 |
2020-01-31 19:58:09 Iteration 2350 Training Loss: 6.401e-02 Loss in Target Net: 4.613e-03 |
2020-01-31 19:58:31 Iteration 2400 Training Loss: 6.460e-02 Loss in Target Net: 4.603e-03 |
2020-01-31 19:58:52 Iteration 2450 Training Loss: 5.962e-02 Loss in Target Net: 5.361e-03 |
2020-01-31 19:59:13 Iteration 2500 Training Loss: 6.804e-02 Loss in Target Net: 4.550e-03 |
2020-01-31 19:59:34 Iteration 2550 Training Loss: 6.719e-02 Loss in Target Net: 7.489e-03 |
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2020-01-31 20:00:17 Iteration 2650 Training Loss: 6.327e-02 Loss in Target Net: 5.847e-03 |
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2020-01-31 20:00:59 Iteration 2750 Training Loss: 6.429e-02 Loss in Target Net: 6.040e-03 |
2020-01-31 20:01:20 Iteration 2800 Training Loss: 6.609e-02 Loss in Target Net: 8.015e-03 |
2020-01-31 20:01:41 Iteration 2850 Training Loss: 6.471e-02 Loss in Target Net: 3.839e-03 |
2020-01-31 20:02:03 Iteration 2900 Training Loss: 6.692e-02 Loss in Target Net: 3.829e-03 |
2020-01-31 20:02:24 Iteration 2950 Training Loss: 5.938e-02 Loss in Target Net: 3.816e-03 |
2020-01-31 20:02:45 Iteration 3000 Training Loss: 5.984e-02 Loss in Target Net: 3.319e-03 |
2020-01-31 20:03:06 Iteration 3050 Training Loss: 5.797e-02 Loss in Target Net: 4.876e-03 |
2020-01-31 20:03:28 Iteration 3100 Training Loss: 6.033e-02 Loss in Target Net: 6.480e-03 |
2020-01-31 20:03:49 Iteration 3150 Training Loss: 6.360e-02 Loss in Target Net: 3.520e-03 |
2020-01-31 20:04:10 Iteration 3200 Training Loss: 6.279e-02 Loss in Target Net: 4.749e-03 |
2020-01-31 20:04:31 Iteration 3250 Training Loss: 6.198e-02 Loss in Target Net: 4.822e-03 |
2020-01-31 20:04:53 Iteration 3300 Training Loss: 6.144e-02 Loss in Target Net: 5.767e-03 |
2020-01-31 20:05:14 Iteration 3350 Training Loss: 6.598e-02 Loss in Target Net: 4.404e-03 |
2020-01-31 20:05:36 Iteration 3400 Training Loss: 6.105e-02 Loss in Target Net: 7.978e-03 |
2020-01-31 20:05:57 Iteration 3450 Training Loss: 6.872e-02 Loss in Target Net: 7.444e-03 |
2020-01-31 20:06:18 Iteration 3500 Training Loss: 6.101e-02 Loss in Target Net: 5.699e-03 |
2020-01-31 20:06:39 Iteration 3550 Training Loss: 6.360e-02 Loss in Target Net: 6.284e-03 |
2020-01-31 20:07:01 Iteration 3600 Training Loss: 6.181e-02 Loss in Target Net: 6.968e-03 |
2020-01-31 20:07:22 Iteration 3650 Training Loss: 6.429e-02 Loss in Target Net: 6.203e-03 |
2020-01-31 20:07:43 Iteration 3700 Training Loss: 5.989e-02 Loss in Target Net: 8.625e-03 |
2020-01-31 20:08:04 Iteration 3750 Training Loss: 5.990e-02 Loss in Target Net: 5.160e-03 |
2020-01-31 20:08:25 Iteration 3800 Training Loss: 6.124e-02 Loss in Target Net: 6.403e-03 |
2020-01-31 20:08:46 Iteration 3850 Training Loss: 6.025e-02 Loss in Target Net: 4.062e-03 |
2020-01-31 20:09:07 Iteration 3900 Training Loss: 6.137e-02 Loss in Target Net: 5.401e-03 |
2020-01-31 20:09:28 Iteration 3950 Training Loss: 6.001e-02 Loss in Target Net: 5.044e-03 |
2020-01-31 20:09:49 Iteration 3999 Training Loss: 6.161e-02 Loss in Target Net: 4.550e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:09:53, Epoch 0, Iteration 7, loss 1.821 (3.236), acc 92.308 (72.400) |
2020-01-31 20:09:53, Epoch 30, Iteration 7, loss 0.021 (0.118), acc 100.000 (97.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[23.376778, -6.829092, -55.734673, -2.547535, -34.278522, -1.3189125, 32.84034, -51.66738, 31.07946, -103.847595], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 20:09:57 Epoch 59, Val iteration 0, acc 92.000 (92.000) |
2020-01-31 20:10:04 Epoch 59, Val iteration 19, acc 92.400 (92.550) |
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