text stringlengths 5 1.13k |
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2020-01-31 20:43:26 Epoch 59, Val iteration 0, acc 88.400 (88.400) |
2020-01-31 20:43:28 Epoch 59, Val iteration 19, acc 88.400 (87.030) |
* Prec: 87.03000106811524 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:43:30, Epoch 0, Iteration 7, loss 1.144 (0.738), acc 92.308 (84.200) |
2020-01-31 20:43:30, Epoch 30, Iteration 7, loss 0.005 (0.043), acc 100.000 (98.400) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-37.469368, -6.1463623, -14.856284, 1.8554826, -38.355774, -6.516959, 11.504903, -23.86747, 11.5158615, -40.155396], Poisons' Predictions:[6, 6, 8, 8, 8] |
2020-01-31 20:43:31 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 20:43:33 Epoch 59, Val iteration 19, acc 93.600 (92.880) |
* Prec: 92.88000106811523 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:43:36, Epoch 0, Iteration 7, loss 0.650 (0.378), acc 94.231 (91.600) |
2020-01-31 20:43:36, Epoch 30, Iteration 7, loss 0.005 (0.005), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-9.945077, -16.687967, -21.760096, -7.2696943, -7.035827, -7.5183516, 5.3687053, -40.309467, 3.9440916, -15.149642], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 20:43:38 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-01-31 20:43:42 Epoch 59, Val iteration 19, acc 93.400 (93.120) |
* Prec: 93.12000122070313 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 25 |
DPN92 1 |
SENet18 0 |
ResNet50 1 |
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='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=26, 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/4000/26 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 20:15:33 Iteration 0 Training Loss: 1.141e+00 Loss in Target Net: 3.636e-01 |
2020-01-31 20:15:55 Iteration 50 Training Loss: 1.223e-01 Loss in Target Net: 1.434e-02 |
2020-01-31 20:16:17 Iteration 100 Training Loss: 1.059e-01 Loss in Target Net: 9.050e-03 |
2020-01-31 20:16:39 Iteration 150 Training Loss: 9.562e-02 Loss in Target Net: 1.047e-02 |
2020-01-31 20:17:01 Iteration 200 Training Loss: 1.010e-01 Loss in Target Net: 9.171e-03 |
2020-01-31 20:17:23 Iteration 250 Training Loss: 1.063e-01 Loss in Target Net: 1.428e-02 |
2020-01-31 20:17:44 Iteration 300 Training Loss: 9.553e-02 Loss in Target Net: 9.254e-03 |
2020-01-31 20:18:07 Iteration 350 Training Loss: 8.986e-02 Loss in Target Net: 5.880e-03 |
2020-01-31 20:18:29 Iteration 400 Training Loss: 9.267e-02 Loss in Target Net: 8.796e-03 |
2020-01-31 20:18:51 Iteration 450 Training Loss: 9.231e-02 Loss in Target Net: 7.849e-03 |
2020-01-31 20:19:13 Iteration 500 Training Loss: 9.950e-02 Loss in Target Net: 7.944e-03 |
2020-01-31 20:19:34 Iteration 550 Training Loss: 9.820e-02 Loss in Target Net: 5.924e-03 |
2020-01-31 20:19:56 Iteration 600 Training Loss: 8.813e-02 Loss in Target Net: 6.477e-03 |
2020-01-31 20:20:18 Iteration 650 Training Loss: 9.978e-02 Loss in Target Net: 8.912e-03 |
2020-01-31 20:20:41 Iteration 700 Training Loss: 8.683e-02 Loss in Target Net: 8.820e-03 |
2020-01-31 20:21:03 Iteration 750 Training Loss: 1.015e-01 Loss in Target Net: 5.807e-03 |
2020-01-31 20:21:25 Iteration 800 Training Loss: 8.796e-02 Loss in Target Net: 9.214e-03 |
2020-01-31 20:21:47 Iteration 850 Training Loss: 8.926e-02 Loss in Target Net: 7.889e-03 |
2020-01-31 20:22:09 Iteration 900 Training Loss: 9.090e-02 Loss in Target Net: 7.967e-03 |
2020-01-31 20:22:31 Iteration 950 Training Loss: 8.969e-02 Loss in Target Net: 1.022e-02 |
2020-01-31 20:22:53 Iteration 1000 Training Loss: 8.825e-02 Loss in Target Net: 6.659e-03 |
2020-01-31 20:23:15 Iteration 1050 Training Loss: 9.145e-02 Loss in Target Net: 6.232e-03 |
2020-01-31 20:23:36 Iteration 1100 Training Loss: 9.869e-02 Loss in Target Net: 7.690e-03 |
2020-01-31 20:23:58 Iteration 1150 Training Loss: 9.378e-02 Loss in Target Net: 8.528e-03 |
2020-01-31 20:24:20 Iteration 1200 Training Loss: 9.312e-02 Loss in Target Net: 1.344e-02 |
2020-01-31 20:24:42 Iteration 1250 Training Loss: 8.968e-02 Loss in Target Net: 8.176e-03 |
2020-01-31 20:25:04 Iteration 1300 Training Loss: 8.934e-02 Loss in Target Net: 9.474e-03 |
2020-01-31 20:25:25 Iteration 1350 Training Loss: 9.002e-02 Loss in Target Net: 1.100e-02 |
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2020-01-31 20:26:09 Iteration 1450 Training Loss: 9.111e-02 Loss in Target Net: 6.879e-03 |
2020-01-31 20:26:31 Iteration 1500 Training Loss: 9.060e-02 Loss in Target Net: 1.405e-02 |
2020-01-31 20:26:53 Iteration 1550 Training Loss: 8.413e-02 Loss in Target Net: 8.060e-03 |
2020-01-31 20:27:15 Iteration 1600 Training Loss: 8.733e-02 Loss in Target Net: 9.107e-03 |
2020-01-31 20:27:37 Iteration 1650 Training Loss: 8.963e-02 Loss in Target Net: 4.673e-03 |
2020-01-31 20:27:58 Iteration 1700 Training Loss: 9.179e-02 Loss in Target Net: 7.114e-03 |
2020-01-31 20:28:20 Iteration 1750 Training Loss: 8.282e-02 Loss in Target Net: 8.210e-03 |
2020-01-31 20:28:42 Iteration 1800 Training Loss: 8.869e-02 Loss in Target Net: 9.076e-03 |
2020-01-31 20:29:04 Iteration 1850 Training Loss: 8.526e-02 Loss in Target Net: 6.399e-03 |
2020-01-31 20:29:26 Iteration 1900 Training Loss: 8.179e-02 Loss in Target Net: 8.503e-03 |
2020-01-31 20:29:48 Iteration 1950 Training Loss: 8.870e-02 Loss in Target Net: 7.697e-03 |
2020-01-31 20:30:10 Iteration 2000 Training Loss: 8.475e-02 Loss in Target Net: 1.016e-02 |
2020-01-31 20:30:32 Iteration 2050 Training Loss: 8.341e-02 Loss in Target Net: 1.076e-02 |
2020-01-31 20:30:54 Iteration 2100 Training Loss: 8.673e-02 Loss in Target Net: 7.552e-03 |
2020-01-31 20:31:15 Iteration 2150 Training Loss: 8.450e-02 Loss in Target Net: 8.494e-03 |
2020-01-31 20:31:37 Iteration 2200 Training Loss: 8.939e-02 Loss in Target Net: 9.066e-03 |
2020-01-31 20:31:59 Iteration 2250 Training Loss: 8.855e-02 Loss in Target Net: 6.761e-03 |
2020-01-31 20:32:21 Iteration 2300 Training Loss: 8.584e-02 Loss in Target Net: 1.288e-02 |
2020-01-31 20:32:43 Iteration 2350 Training Loss: 9.029e-02 Loss in Target Net: 1.257e-02 |
2020-01-31 20:33:04 Iteration 2400 Training Loss: 8.921e-02 Loss in Target Net: 8.896e-03 |
2020-01-31 20:33:26 Iteration 2450 Training Loss: 9.347e-02 Loss in Target Net: 1.192e-02 |
2020-01-31 20:33:48 Iteration 2500 Training Loss: 9.530e-02 Loss in Target Net: 9.118e-03 |
2020-01-31 20:34:10 Iteration 2550 Training Loss: 8.806e-02 Loss in Target Net: 9.739e-03 |
2020-01-31 20:34:32 Iteration 2600 Training Loss: 9.449e-02 Loss in Target Net: 7.407e-03 |
2020-01-31 20:34:54 Iteration 2650 Training Loss: 9.021e-02 Loss in Target Net: 1.176e-02 |
2020-01-31 20:35:15 Iteration 2700 Training Loss: 8.382e-02 Loss in Target Net: 6.478e-03 |
2020-01-31 20:35:37 Iteration 2750 Training Loss: 9.018e-02 Loss in Target Net: 1.240e-02 |
2020-01-31 20:35:59 Iteration 2800 Training Loss: 9.017e-02 Loss in Target Net: 9.926e-03 |
2020-01-31 20:36:21 Iteration 2850 Training Loss: 8.769e-02 Loss in Target Net: 1.781e-02 |
2020-01-31 20:36:43 Iteration 2900 Training Loss: 8.899e-02 Loss in Target Net: 9.024e-03 |
2020-01-31 20:37:05 Iteration 2950 Training Loss: 9.251e-02 Loss in Target Net: 7.634e-03 |
2020-01-31 20:37:27 Iteration 3000 Training Loss: 9.255e-02 Loss in Target Net: 1.623e-02 |
2020-01-31 20:37:49 Iteration 3050 Training Loss: 9.070e-02 Loss in Target Net: 1.212e-02 |
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