text stringlengths 5 1.13k |
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* Prec: 92.43000144958496 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:47:03, Epoch 0, Iteration 7, loss 2.629 (3.360), acc 76.923 (63.800) |
2020-01-31 21:47:03, Epoch 30, Iteration 7, loss 0.024 (0.181), acc 100.000 (95.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-8.442034, -4.150345, -0.08741945, 8.920573, -5.9928827, -3.6115086, 17.830084, -21.3561, 12.588438, -25.267979], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-01-31 21:47:04 Epoch 59, Val iteration 0, acc 89.200 (89.200) |
2020-01-31 21:47:06 Epoch 59, Val iteration 19, acc 89.600 (87.180) |
* Prec: 87.18000068664551 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:47:07, Epoch 0, Iteration 7, loss 0.174 (0.602), acc 96.154 (85.200) |
2020-01-31 21:47:08, Epoch 30, Iteration 7, loss 0.053 (0.042), acc 98.077 (98.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-47.041847, -9.404516, -12.476733, 0.36093614, -41.86643, -8.9242325, 13.416705, -24.25574, 10.167158, -53.10128], Poisons' Predictions:[8, 8, 6, 6, 8] |
2020-01-31 21:47:08 Epoch 59, Val iteration 0, acc 93.000 (93.000) |
2020-01-31 21:47:10 Epoch 59, Val iteration 19, acc 93.800 (92.720) |
* Prec: 92.72000198364258 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:47:13, Epoch 0, Iteration 7, loss 0.048 (0.428), acc 98.077 (92.000) |
2020-01-31 21:47:13, 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:[-10.868414, -20.068405, -13.185051, -2.106045, -5.5202546, -2.9460526, 8.232754, -31.652933, 7.065498, -18.109634], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:47:15 Epoch 59, Val iteration 0, acc 93.600 (93.600) |
2020-01-31 21:47:19 Epoch 59, Val iteration 19, acc 93.600 (93.220) |
* Prec: 93.22000198364258 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 32 |
DPN92 0 |
SENet18 0 |
ResNet50 1 |
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='1', 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=33, 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/4000/33 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 21:15:00 Iteration 0 Training Loss: 1.097e+00 Loss in Target Net: 4.264e-01 |
2020-01-31 21:15:24 Iteration 50 Training Loss: 9.798e-02 Loss in Target Net: 2.304e-02 |
2020-01-31 21:15:46 Iteration 100 Training Loss: 8.880e-02 Loss in Target Net: 4.029e-02 |
2020-01-31 21:16:07 Iteration 150 Training Loss: 8.850e-02 Loss in Target Net: 4.146e-02 |
2020-01-31 21:16:29 Iteration 200 Training Loss: 7.595e-02 Loss in Target Net: 4.460e-02 |
2020-01-31 21:16:51 Iteration 250 Training Loss: 7.781e-02 Loss in Target Net: 3.688e-02 |
2020-01-31 21:17:13 Iteration 300 Training Loss: 7.495e-02 Loss in Target Net: 4.220e-02 |
2020-01-31 21:17:35 Iteration 350 Training Loss: 7.811e-02 Loss in Target Net: 3.681e-02 |
2020-01-31 21:17:57 Iteration 400 Training Loss: 7.519e-02 Loss in Target Net: 3.965e-02 |
2020-01-31 21:18:21 Iteration 450 Training Loss: 7.340e-02 Loss in Target Net: 4.835e-02 |
2020-01-31 21:18:43 Iteration 500 Training Loss: 7.187e-02 Loss in Target Net: 4.360e-02 |
2020-01-31 21:19:05 Iteration 550 Training Loss: 7.946e-02 Loss in Target Net: 3.480e-02 |
2020-01-31 21:19:28 Iteration 600 Training Loss: 7.324e-02 Loss in Target Net: 3.587e-02 |
2020-01-31 21:19:50 Iteration 650 Training Loss: 7.377e-02 Loss in Target Net: 3.925e-02 |
2020-01-31 21:20:13 Iteration 700 Training Loss: 6.696e-02 Loss in Target Net: 4.465e-02 |
2020-01-31 21:20:35 Iteration 750 Training Loss: 7.028e-02 Loss in Target Net: 4.233e-02 |
2020-01-31 21:20:58 Iteration 800 Training Loss: 7.334e-02 Loss in Target Net: 4.232e-02 |
2020-01-31 21:21:20 Iteration 850 Training Loss: 7.081e-02 Loss in Target Net: 4.906e-02 |
2020-01-31 21:21:42 Iteration 900 Training Loss: 7.263e-02 Loss in Target Net: 4.362e-02 |
2020-01-31 21:22:07 Iteration 950 Training Loss: 6.784e-02 Loss in Target Net: 4.010e-02 |
2020-01-31 21:22:28 Iteration 1000 Training Loss: 7.532e-02 Loss in Target Net: 3.430e-02 |
2020-01-31 21:22:50 Iteration 1050 Training Loss: 7.081e-02 Loss in Target Net: 3.620e-02 |
2020-01-31 21:23:11 Iteration 1100 Training Loss: 7.443e-02 Loss in Target Net: 3.858e-02 |
2020-01-31 21:23:33 Iteration 1150 Training Loss: 6.888e-02 Loss in Target Net: 4.398e-02 |
2020-01-31 21:23:55 Iteration 1200 Training Loss: 7.320e-02 Loss in Target Net: 4.922e-02 |
2020-01-31 21:24:16 Iteration 1250 Training Loss: 7.475e-02 Loss in Target Net: 4.571e-02 |
2020-01-31 21:24:37 Iteration 1300 Training Loss: 7.012e-02 Loss in Target Net: 4.865e-02 |
2020-01-31 21:25:00 Iteration 1350 Training Loss: 7.029e-02 Loss in Target Net: 4.807e-02 |
2020-01-31 21:25:22 Iteration 1400 Training Loss: 7.049e-02 Loss in Target Net: 5.202e-02 |
2020-01-31 21:25:44 Iteration 1450 Training Loss: 7.921e-02 Loss in Target Net: 4.000e-02 |
2020-01-31 21:26:07 Iteration 1500 Training Loss: 7.227e-02 Loss in Target Net: 4.556e-02 |
2020-01-31 21:26:28 Iteration 1550 Training Loss: 7.342e-02 Loss in Target Net: 3.726e-02 |
2020-01-31 21:26:50 Iteration 1600 Training Loss: 7.593e-02 Loss in Target Net: 4.033e-02 |
2020-01-31 21:27:13 Iteration 1650 Training Loss: 7.382e-02 Loss in Target Net: 3.484e-02 |
2020-01-31 21:27:34 Iteration 1700 Training Loss: 7.101e-02 Loss in Target Net: 3.997e-02 |
2020-01-31 21:27:55 Iteration 1750 Training Loss: 6.517e-02 Loss in Target Net: 2.972e-02 |
2020-01-31 21:28:18 Iteration 1800 Training Loss: 7.694e-02 Loss in Target Net: 4.389e-02 |
2020-01-31 21:28:40 Iteration 1850 Training Loss: 7.355e-02 Loss in Target Net: 4.265e-02 |
2020-01-31 21:29:01 Iteration 1900 Training Loss: 6.938e-02 Loss in Target Net: 3.744e-02 |
2020-01-31 21:29:23 Iteration 1950 Training Loss: 7.665e-02 Loss in Target Net: 3.778e-02 |
2020-01-31 21:29:45 Iteration 2000 Training Loss: 7.304e-02 Loss in Target Net: 3.783e-02 |
2020-01-31 21:30:06 Iteration 2050 Training Loss: 7.347e-02 Loss in Target Net: 4.566e-02 |
2020-01-31 21:30:27 Iteration 2100 Training Loss: 6.761e-02 Loss in Target Net: 4.187e-02 |
2020-01-31 21:30:50 Iteration 2150 Training Loss: 7.376e-02 Loss in Target Net: 3.930e-02 |
2020-01-31 21:31:13 Iteration 2200 Training Loss: 6.941e-02 Loss in Target Net: 4.896e-02 |
2020-01-31 21:31:36 Iteration 2250 Training Loss: 6.292e-02 Loss in Target Net: 3.787e-02 |
2020-01-31 21:31:59 Iteration 2300 Training Loss: 6.862e-02 Loss in Target Net: 4.726e-02 |
2020-01-31 21:32:23 Iteration 2350 Training Loss: 6.654e-02 Loss in Target Net: 5.097e-02 |
2020-01-31 21:32:46 Iteration 2400 Training Loss: 7.467e-02 Loss in Target Net: 4.968e-02 |
2020-01-31 21:33:09 Iteration 2450 Training Loss: 6.767e-02 Loss in Target Net: 3.930e-02 |
2020-01-31 21:33:32 Iteration 2500 Training Loss: 6.927e-02 Loss in Target Net: 5.191e-02 |
2020-01-31 21:33:56 Iteration 2550 Training Loss: 7.073e-02 Loss in Target Net: 3.688e-02 |
2020-01-31 21:34:19 Iteration 2600 Training Loss: 7.026e-02 Loss in Target Net: 6.018e-02 |
2020-01-31 21:34:42 Iteration 2650 Training Loss: 6.823e-02 Loss in Target Net: 6.213e-02 |
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