SunayBhat1/PureGen_Poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
2020-01-31 17:42:49, Epoch 30, Iteration 7, loss 0.000 (0.005), acc 100.000 (99.600)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-54.637634, -20.816061, -44.05202, -22.386467, -60.55769, -39.561966, 19.719904, 0.7524526, 19.84206, -61.229282], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 17:42:50 Epoch 59, Val iteration 0, acc 93.000 (93.000)
2020-01-31 17:42:54 Epoch 59, Val iteration 19, acc 94.400 (92.860)
* Prec: 92.86000175476075
--------
ResNeXt29_2x64d
Using Adam for retraining
Files already downloaded and verified
2020-01-31 17:42:57, Epoch 0, Iteration 7, loss 0.322 (1.508), acc 94.231 (79.400)
2020-01-31 17:42:57, Epoch 30, Iteration 7, loss 0.013 (0.073), acc 100.000 (98.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-23.987703, 17.416262, -17.761349, 8.190687, -63.61976, -34.748165, 30.653288, -35.612022, 29.687984, -34.126457], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 17:42:59 Epoch 59, Val iteration 0, acc 93.400 (93.400)
2020-01-31 17:43:03 Epoch 59, Val iteration 19, acc 93.400 (93.420)
* Prec: 93.42000236511231
--------
GoogLeNet
Using Adam for retraining
Files already downloaded and verified
2020-01-31 17:43:06, Epoch 0, Iteration 7, loss 0.636 (0.448), acc 88.462 (90.400)
2020-01-31 17:43:06, Epoch 30, Iteration 7, loss 0.002 (0.047), acc 100.000 (98.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-12.318884, -5.914258, -6.4106803, -1.699879, -10.782881, -5.2274313, 8.296249, -4.3582096, 4.627821, -16.890987], Poisons' Predictions:[8, 6, 8, 8, 8]
2020-01-31 17:43:09 Epoch 59, Val iteration 0, acc 91.800 (91.800)
2020-01-31 17:43:13 Epoch 59, Val iteration 19, acc 92.600 (92.190)
* Prec: 92.19000129699707
--------
MobileNetV2
Using Adam for retraining
Files already downloaded and verified
2020-01-31 17:43:16, Epoch 0, Iteration 7, loss 1.265 (3.677), acc 76.923 (62.200)
2020-01-31 17:43:16, Epoch 30, Iteration 7, loss 0.121 (0.208), acc 94.231 (94.400)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.7937117, -18.993473, -2.7967765, 16.094019, -12.925326, -3.4080544, 26.977407, -25.139593, 15.99573, -25.121454], Poisons' Predictions:[8, 8, 6, 8, 8]
2020-01-31 17:43:17 Epoch 59, Val iteration 0, acc 87.800 (87.800)
2020-01-31 17:43:19 Epoch 59, Val iteration 19, acc 87.600 (86.880)
* Prec: 86.88000144958497
--------
ResNet18
Using Adam for retraining
Files already downloaded and verified
2020-01-31 17:43:21, Epoch 0, Iteration 7, loss 0.062 (0.555), acc 98.077 (90.000)
2020-01-31 17:43:21, Epoch 30, Iteration 7, loss 0.127 (0.068), acc 96.154 (98.600)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-22.230066, -19.710695, -14.40099, -3.8324134, -38.67678, -12.537973, 6.796923, -24.991034, 9.23048, -51.40395], Poisons' Predictions:[8, 6, 8, 8, 8]
2020-01-31 17:43:21 Epoch 59, Val iteration 0, acc 93.800 (93.800)
2020-01-31 17:43:23 Epoch 59, Val iteration 19, acc 93.800 (92.710)
* Prec: 92.71000137329102
--------
DenseNet121
Using Adam for retraining
Files already downloaded and verified
2020-01-31 17:43:26, Epoch 0, Iteration 7, loss 0.134 (0.357), acc 98.077 (93.200)
2020-01-31 17:43:26, Epoch 30, Iteration 7, loss 0.001 (0.004), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-6.9463587, -14.749203, -9.482195, -0.8062791, -11.665554, -5.789773, 6.1873198, -26.732347, 3.916795, -21.543924], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 17:43:28 Epoch 59, Val iteration 0, acc 94.200 (94.200)
2020-01-31 17:43:32 Epoch 59, Val iteration 19, acc 92.800 (93.010)
* Prec: 93.01000213623047
--------
------SUMMARY------
TIME ELAPSED (mins): 31
TARGET INDEX: 0
DPN92 0
SENet18 1
ResNet50 1
ResNeXt29_2x64d 0
GoogLeNet 0
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='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=1, 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/4000/1
Selected base image indices: [213, 225, 227, 247, 249]
2020-01-31 17:11:04 Iteration 0 Training Loss: 1.094e+00 Loss in Target Net: 4.584e-01
2020-01-31 17:11:26 Iteration 50 Training Loss: 9.423e-02 Loss in Target Net: 1.330e-02
2020-01-31 17:11:48 Iteration 100 Training Loss: 7.994e-02 Loss in Target Net: 1.087e-02
2020-01-31 17:12:10 Iteration 150 Training Loss: 8.202e-02 Loss in Target Net: 1.569e-02
2020-01-31 17:12:31 Iteration 200 Training Loss: 8.420e-02 Loss in Target Net: 1.593e-02
2020-01-31 17:12:54 Iteration 250 Training Loss: 7.825e-02 Loss in Target Net: 2.233e-02
2020-01-31 17:13:16 Iteration 300 Training Loss: 7.690e-02 Loss in Target Net: 1.585e-02
2020-01-31 17:13:39 Iteration 350 Training Loss: 8.546e-02 Loss in Target Net: 9.229e-03
2020-01-31 17:14:00 Iteration 400 Training Loss: 8.008e-02 Loss in Target Net: 1.432e-02
2020-01-31 17:14:22 Iteration 450 Training Loss: 7.371e-02 Loss in Target Net: 9.999e-03
2020-01-31 17:14:44 Iteration 500 Training Loss: 7.370e-02 Loss in Target Net: 1.612e-02
2020-01-31 17:15:06 Iteration 550 Training Loss: 7.728e-02 Loss in Target Net: 1.655e-02
2020-01-31 17:15:28 Iteration 600 Training Loss: 7.427e-02 Loss in Target Net: 2.325e-02
2020-01-31 17:15:50 Iteration 650 Training Loss: 7.618e-02 Loss in Target Net: 1.922e-02
2020-01-31 17:16:12 Iteration 700 Training Loss: 6.773e-02 Loss in Target Net: 1.156e-02
2020-01-31 17:16:33 Iteration 750 Training Loss: 7.670e-02 Loss in Target Net: 1.608e-02
2020-01-31 17:16:55 Iteration 800 Training Loss: 7.448e-02 Loss in Target Net: 1.224e-02
2020-01-31 17:17:17 Iteration 850 Training Loss: 7.556e-02 Loss in Target Net: 1.028e-02
2020-01-31 17:17:39 Iteration 900 Training Loss: 7.380e-02 Loss in Target Net: 6.635e-03
2020-01-31 17:18:00 Iteration 950 Training Loss: 8.174e-02 Loss in Target Net: 8.967e-03
2020-01-31 17:18:23 Iteration 1000 Training Loss: 7.514e-02 Loss in Target Net: 1.144e-02
2020-01-31 17:18:44 Iteration 1050 Training Loss: 7.362e-02 Loss in Target Net: 1.001e-02
2020-01-31 17:19:06 Iteration 1100 Training Loss: 7.597e-02 Loss in Target Net: 7.279e-03
2020-01-31 17:19:28 Iteration 1150 Training Loss: 7.489e-02 Loss in Target Net: 8.267e-03
2020-01-31 17:19:50 Iteration 1200 Training Loss: 7.441e-02 Loss in Target Net: 1.241e-02
2020-01-31 17:20:11 Iteration 1250 Training Loss: 7.666e-02 Loss in Target Net: 8.267e-03
2020-01-31 17:20:33 Iteration 1300 Training Loss: 6.977e-02 Loss in Target Net: 1.210e-02
2020-01-31 17:20:55 Iteration 1350 Training Loss: 7.436e-02 Loss in Target Net: 1.359e-02
2020-01-31 17:21:17 Iteration 1400 Training Loss: 7.193e-02 Loss in Target Net: 1.277e-02
2020-01-31 17:21:39 Iteration 1450 Training Loss: 7.116e-02 Loss in Target Net: 8.619e-03

2020-01-31 17:42:49, Epoch 30, Iteration 7, loss 0.000 (0.005), acc 100.000 (99.600)

Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-54.637634, -20.816061, -44.05202, -22.386467, -60.55769, -39.561966, 19.719904, 0.7524526, 19.84206, -61.229282], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-01-31 17:42:50 Epoch 59, Val iteration 0, acc 93.000 (93.000)

2020-01-31 17:42:54 Epoch 59, Val iteration 19, acc 94.400 (92.860)

* Prec: 92.86000175476075

--------

ResNeXt29_2x64d

Using Adam for retraining

Files already downloaded and verified

2020-01-31 17:42:57, Epoch 0, Iteration 7, loss 0.322 (1.508), acc 94.231 (79.400)

2020-01-31 17:42:57, Epoch 30, Iteration 7, loss 0.013 (0.073), acc 100.000 (98.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-23.987703, 17.416262, -17.761349, 8.190687, -63.61976, -34.748165, 30.653288, -35.612022, 29.687984, -34.126457], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-01-31 17:42:59 Epoch 59, Val iteration 0, acc 93.400 (93.400)

2020-01-31 17:43:03 Epoch 59, Val iteration 19, acc 93.400 (93.420)

* Prec: 93.42000236511231

--------

GoogLeNet

Using Adam for retraining

Files already downloaded and verified

2020-01-31 17:43:06, Epoch 0, Iteration 7, loss 0.636 (0.448), acc 88.462 (90.400)

2020-01-31 17:43:06, Epoch 30, Iteration 7, loss 0.002 (0.047), acc 100.000 (98.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-12.318884, -5.914258, -6.4106803, -1.699879, -10.782881, -5.2274313, 8.296249, -4.3582096, 4.627821, -16.890987], Poisons' Predictions:[8, 6, 8, 8, 8]

2020-01-31 17:43:09 Epoch 59, Val iteration 0, acc 91.800 (91.800)

2020-01-31 17:43:13 Epoch 59, Val iteration 19, acc 92.600 (92.190)

* Prec: 92.19000129699707

--------

MobileNetV2

Using Adam for retraining

Files already downloaded and verified

2020-01-31 17:43:16, Epoch 0, Iteration 7, loss 1.265 (3.677), acc 76.923 (62.200)

2020-01-31 17:43:16, Epoch 30, Iteration 7, loss 0.121 (0.208), acc 94.231 (94.400)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.7937117, -18.993473, -2.7967765, 16.094019, -12.925326, -3.4080544, 26.977407, -25.139593, 15.99573, -25.121454], Poisons' Predictions:[8, 8, 6, 8, 8]

2020-01-31 17:43:17 Epoch 59, Val iteration 0, acc 87.800 (87.800)

2020-01-31 17:43:19 Epoch 59, Val iteration 19, acc 87.600 (86.880)

* Prec: 86.88000144958497

--------

ResNet18

Using Adam for retraining

Files already downloaded and verified

2020-01-31 17:43:21, Epoch 0, Iteration 7, loss 0.062 (0.555), acc 98.077 (90.000)

2020-01-31 17:43:21, Epoch 30, Iteration 7, loss 0.127 (0.068), acc 96.154 (98.600)

Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-22.230066, -19.710695, -14.40099, -3.8324134, -38.67678, -12.537973, 6.796923, -24.991034, 9.23048, -51.40395], Poisons' Predictions:[8, 6, 8, 8, 8]

2020-01-31 17:43:21 Epoch 59, Val iteration 0, acc 93.800 (93.800)

2020-01-31 17:43:23 Epoch 59, Val iteration 19, acc 93.800 (92.710)

* Prec: 92.71000137329102

--------

DenseNet121

Using Adam for retraining

Files already downloaded and verified

2020-01-31 17:43:26, Epoch 0, Iteration 7, loss 0.134 (0.357), acc 98.077 (93.200)

2020-01-31 17:43:26, Epoch 30, Iteration 7, loss 0.001 (0.004), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-6.9463587, -14.749203, -9.482195, -0.8062791, -11.665554, -5.789773, 6.1873198, -26.732347, 3.916795, -21.543924], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-01-31 17:43:28 Epoch 59, Val iteration 0, acc 94.200 (94.200)

2020-01-31 17:43:32 Epoch 59, Val iteration 19, acc 92.800 (93.010)

* Prec: 93.01000213623047

--------

------SUMMARY------

TIME ELAPSED (mins): 31

TARGET INDEX: 0

DPN92 0

SENet18 1

ResNet50 1

ResNeXt29_2x64d 0

GoogLeNet 0

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='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=1, 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/4000/1

Selected base image indices: [213, 225, 227, 247, 249]

2020-01-31 17:11:04 Iteration 0 Training Loss: 1.094e+00 Loss in Target Net: 4.584e-01

2020-01-31 17:11:26 Iteration 50 Training Loss: 9.423e-02 Loss in Target Net: 1.330e-02

2020-01-31 17:11:48 Iteration 100 Training Loss: 7.994e-02 Loss in Target Net: 1.087e-02

2020-01-31 17:12:10 Iteration 150 Training Loss: 8.202e-02 Loss in Target Net: 1.569e-02

2020-01-31 17:12:31 Iteration 200 Training Loss: 8.420e-02 Loss in Target Net: 1.593e-02

2020-01-31 17:12:54 Iteration 250 Training Loss: 7.825e-02 Loss in Target Net: 2.233e-02

2020-01-31 17:13:16 Iteration 300 Training Loss: 7.690e-02 Loss in Target Net: 1.585e-02

2020-01-31 17:13:39 Iteration 350 Training Loss: 8.546e-02 Loss in Target Net: 9.229e-03

2020-01-31 17:14:00 Iteration 400 Training Loss: 8.008e-02 Loss in Target Net: 1.432e-02

2020-01-31 17:14:22 Iteration 450 Training Loss: 7.371e-02 Loss in Target Net: 9.999e-03

2020-01-31 17:14:44 Iteration 500 Training Loss: 7.370e-02 Loss in Target Net: 1.612e-02

2020-01-31 17:15:06 Iteration 550 Training Loss: 7.728e-02 Loss in Target Net: 1.655e-02

2020-01-31 17:15:28 Iteration 600 Training Loss: 7.427e-02 Loss in Target Net: 2.325e-02

2020-01-31 17:15:50 Iteration 650 Training Loss: 7.618e-02 Loss in Target Net: 1.922e-02

2020-01-31 17:16:12 Iteration 700 Training Loss: 6.773e-02 Loss in Target Net: 1.156e-02

2020-01-31 17:16:33 Iteration 750 Training Loss: 7.670e-02 Loss in Target Net: 1.608e-02

2020-01-31 17:16:55 Iteration 800 Training Loss: 7.448e-02 Loss in Target Net: 1.224e-02

2020-01-31 17:17:17 Iteration 850 Training Loss: 7.556e-02 Loss in Target Net: 1.028e-02

2020-01-31 17:17:39 Iteration 900 Training Loss: 7.380e-02 Loss in Target Net: 6.635e-03

2020-01-31 17:18:00 Iteration 950 Training Loss: 8.174e-02 Loss in Target Net: 8.967e-03

2020-01-31 17:18:23 Iteration 1000 Training Loss: 7.514e-02 Loss in Target Net: 1.144e-02

2020-01-31 17:18:44 Iteration 1050 Training Loss: 7.362e-02 Loss in Target Net: 1.001e-02

2020-01-31 17:19:06 Iteration 1100 Training Loss: 7.597e-02 Loss in Target Net: 7.279e-03

2020-01-31 17:19:28 Iteration 1150 Training Loss: 7.489e-02 Loss in Target Net: 8.267e-03

2020-01-31 17:19:50 Iteration 1200 Training Loss: 7.441e-02 Loss in Target Net: 1.241e-02

2020-01-31 17:20:11 Iteration 1250 Training Loss: 7.666e-02 Loss in Target Net: 8.267e-03

2020-01-31 17:20:33 Iteration 1300 Training Loss: 6.977e-02 Loss in Target Net: 1.210e-02

2020-01-31 17:20:55 Iteration 1350 Training Loss: 7.436e-02 Loss in Target Net: 1.359e-02

2020-01-31 17:21:17 Iteration 1400 Training Loss: 7.193e-02 Loss in Target Net: 1.277e-02

2020-01-31 17:21:39 Iteration 1450 Training Loss: 7.116e-02 Loss in Target Net: 8.619e-03