text stringlengths 5 1.13k |
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Files already downloaded and verified |
2020-01-31 17:41:40, Epoch 0, Iteration 7, loss 0.677 (0.646), acc 90.385 (88.400) |
2020-01-31 17:41:40, Epoch 30, Iteration 7, loss 0.263 (0.260), acc 94.231 (95.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[2.3395412, -1.6410697, -2.803746, -2.636074, 1.4307746, -10.535016, 21.207388, -15.590498, 16.747263, -11.365155], Poisons' Predictions:[8, 6, 6, 8, 6] |
2020-01-31 17:41:41 Epoch 59, Val iteration 0, acc 92.000 (92.000) |
2020-01-31 17:41:43 Epoch 59, Val iteration 19, acc 92.800 (91.330) |
* Prec: 91.33000144958496 |
-------- |
ResNet50 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:41:45, Epoch 0, Iteration 7, loss 0.000 (1.049), acc 100.000 (87.000) |
2020-01-31 17:41:45, Epoch 30, Iteration 7, loss 0.072 (0.019), acc 98.077 (99.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-30.144352, -18.544514, -59.352276, -37.8145, -47.761284, -29.23841, 21.051205, -94.66296, 13.735939, -44.02083], Poisons' Predictions:[8, 8, 8, 6, 6] |
2020-01-31 17:41:47 Epoch 59, Val iteration 0, acc 91.400 (91.400) |
2020-01-31 17:41:51 Epoch 59, Val iteration 19, acc 92.600 (91.880) |
* Prec: 91.88000030517578 |
-------- |
ResNeXt29_2x64d |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:41:53, Epoch 0, Iteration 7, loss 0.567 (2.230), acc 90.385 (70.200) |
2020-01-31 17:41:53, Epoch 30, Iteration 7, loss 0.045 (0.086), acc 98.077 (97.400) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-19.881458, -11.728312, -0.3059676, 2.2721138, -65.111046, -21.75241, 22.492073, -14.355262, 24.587753, -15.139097], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-01-31 17:41:55 Epoch 59, Val iteration 0, acc 93.000 (93.000) |
2020-01-31 17:41:59 Epoch 59, Val iteration 19, acc 92.600 (92.880) |
* Prec: 92.8800006866455 |
-------- |
GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:42:01, Epoch 0, Iteration 7, loss 0.289 (0.376), acc 94.231 (90.600) |
2020-01-31 17:42:02, Epoch 30, Iteration 7, loss 0.086 (0.055), acc 96.154 (98.400) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-18.68445, -17.553432, -4.783514, -2.482345, -6.8569384, -1.6085731, -0.9537451, -8.12099, 7.610975, -12.457383], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 17:42:04 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-01-31 17:42:09 Epoch 59, Val iteration 19, acc 92.000 (92.370) |
* Prec: 92.3700023651123 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:42:11, Epoch 0, Iteration 7, loss 1.724 (2.667), acc 80.769 (69.600) |
2020-01-31 17:42:11, Epoch 30, Iteration 7, loss 0.273 (0.533), acc 92.308 (89.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-5.0710573, -18.270926, 4.4129214, 8.042304, -18.64729, -11.518337, 16.878654, -39.30454, 11.903157, -27.490608], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 17:42:12 Epoch 59, Val iteration 0, acc 88.800 (88.800) |
2020-01-31 17:42:14 Epoch 59, Val iteration 19, acc 89.000 (87.180) |
* Prec: 87.18000144958496 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:42:16, Epoch 0, Iteration 7, loss 0.112 (0.621), acc 92.308 (86.000) |
2020-01-31 17:42:16, Epoch 30, Iteration 7, loss 0.012 (0.017), acc 100.000 (99.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-45.408817, -24.166622, -11.601466, 5.7699895, -36.002293, -11.801348, 15.323441, -13.602728, 13.640121, -31.797855], Poisons' Predictions:[8, 6, 8, 6, 8] |
2020-01-31 17:42:16 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-01-31 17:42:18 Epoch 59, Val iteration 19, acc 93.800 (92.680) |
* Prec: 92.68000106811523 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 17:42:21, Epoch 0, Iteration 7, loss 0.571 (0.359), acc 94.231 (93.200) |
2020-01-31 17:42:21, Epoch 30, Iteration 7, loss 0.004 (0.005), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-14.239947, -15.120184, -20.58574, -4.7334805, -17.943935, -8.631443, 6.5526304, -42.611805, 3.7673373, -19.736464], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 17:42:23 Epoch 59, Val iteration 0, acc 94.400 (94.400) |
2020-01-31 17:42:28 Epoch 59, Val iteration 19, acc 93.000 (93.040) |
* Prec: 93.0400016784668 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 30 |
TARGET INDEX: 2 |
DPN92 0 |
SENet18 0 |
ResNet50 0 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
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='0', 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=20, 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/4000/20 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 19:46:37 Iteration 0 Training Loss: 1.063e+00 Loss in Target Net: 4.087e-01 |
2020-01-31 19:46:58 Iteration 50 Training Loss: 1.099e-01 Loss in Target Net: 9.238e-03 |
2020-01-31 19:47:18 Iteration 100 Training Loss: 8.864e-02 Loss in Target Net: 6.481e-03 |
2020-01-31 19:47:38 Iteration 150 Training Loss: 8.536e-02 Loss in Target Net: 6.773e-03 |
2020-01-31 19:47:58 Iteration 200 Training Loss: 8.532e-02 Loss in Target Net: 7.257e-03 |
2020-01-31 19:48:19 Iteration 250 Training Loss: 8.651e-02 Loss in Target Net: 8.975e-03 |
2020-01-31 19:48:39 Iteration 300 Training Loss: 8.208e-02 Loss in Target Net: 5.965e-03 |
2020-01-31 19:48:59 Iteration 350 Training Loss: 8.247e-02 Loss in Target Net: 8.264e-03 |
2020-01-31 19:49:20 Iteration 400 Training Loss: 7.829e-02 Loss in Target Net: 5.404e-03 |
2020-01-31 19:49:40 Iteration 450 Training Loss: 7.818e-02 Loss in Target Net: 5.905e-03 |
2020-01-31 19:50:00 Iteration 500 Training Loss: 8.087e-02 Loss in Target Net: 7.209e-03 |
2020-01-31 19:50:21 Iteration 550 Training Loss: 8.041e-02 Loss in Target Net: 5.781e-03 |
2020-01-31 19:50:42 Iteration 600 Training Loss: 7.518e-02 Loss in Target Net: 5.287e-03 |
2020-01-31 19:51:02 Iteration 650 Training Loss: 8.128e-02 Loss in Target Net: 6.571e-03 |
2020-01-31 19:51:23 Iteration 700 Training Loss: 7.442e-02 Loss in Target Net: 6.133e-03 |
2020-01-31 19:51:43 Iteration 750 Training Loss: 8.399e-02 Loss in Target Net: 6.081e-03 |
2020-01-31 19:52:04 Iteration 800 Training Loss: 8.588e-02 Loss in Target Net: 5.495e-03 |
2020-01-31 19:52:24 Iteration 850 Training Loss: 7.861e-02 Loss in Target Net: 7.602e-03 |
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