SunayBhat1/PureGen_Poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
------SUMMARY------
TIME ELAPSED (mins): 111
TARGET INDEX: 49
DPN92 0
SENet18 0
ResNet50 1
ResNeXt29_2x64d 0
GoogLeNet 0
MobileNetV2 1
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=5, 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/4000/5
Selected base image indices: [213, 225, 227, 247, 249]
2020-01-31 17:41:34 Iteration 0 Training Loss: 1.013e+00 Loss in Target Net: 3.871e-01
2020-01-31 17:41:55 Iteration 50 Training Loss: 1.036e-01 Loss in Target Net: 2.418e-02
2020-01-31 17:42:17 Iteration 100 Training Loss: 8.948e-02 Loss in Target Net: 2.045e-02
2020-01-31 17:42:38 Iteration 150 Training Loss: 8.795e-02 Loss in Target Net: 2.162e-02
2020-01-31 17:42:59 Iteration 200 Training Loss: 9.130e-02 Loss in Target Net: 1.803e-02
2020-01-31 17:43:21 Iteration 250 Training Loss: 8.816e-02 Loss in Target Net: 2.395e-02
2020-01-31 17:43:42 Iteration 300 Training Loss: 8.366e-02 Loss in Target Net: 1.511e-02
2020-01-31 17:44:04 Iteration 350 Training Loss: 7.727e-02 Loss in Target Net: 2.758e-02
2020-01-31 17:44:25 Iteration 400 Training Loss: 8.815e-02 Loss in Target Net: 2.788e-02
2020-01-31 17:44:45 Iteration 450 Training Loss: 7.625e-02 Loss in Target Net: 2.747e-02
2020-01-31 17:45:06 Iteration 500 Training Loss: 7.909e-02 Loss in Target Net: 2.147e-02
2020-01-31 17:45:27 Iteration 550 Training Loss: 7.683e-02 Loss in Target Net: 2.721e-02
2020-01-31 17:45:48 Iteration 600 Training Loss: 8.266e-02 Loss in Target Net: 1.998e-02
2020-01-31 17:46:10 Iteration 650 Training Loss: 7.562e-02 Loss in Target Net: 3.554e-02
2020-01-31 17:46:31 Iteration 700 Training Loss: 7.478e-02 Loss in Target Net: 2.860e-02
2020-01-31 17:46:53 Iteration 750 Training Loss: 7.594e-02 Loss in Target Net: 2.852e-02
2020-01-31 17:47:14 Iteration 800 Training Loss: 8.192e-02 Loss in Target Net: 2.820e-02
2020-01-31 17:47:35 Iteration 850 Training Loss: 7.549e-02 Loss in Target Net: 2.303e-02
2020-01-31 17:47:56 Iteration 900 Training Loss: 7.736e-02 Loss in Target Net: 3.213e-02
2020-01-31 17:48:17 Iteration 950 Training Loss: 7.711e-02 Loss in Target Net: 4.165e-02
2020-01-31 17:48:38 Iteration 1000 Training Loss: 7.549e-02 Loss in Target Net: 2.337e-02
2020-01-31 17:49:00 Iteration 1050 Training Loss: 7.283e-02 Loss in Target Net: 2.736e-02
2020-01-31 17:49:21 Iteration 1100 Training Loss: 8.305e-02 Loss in Target Net: 2.553e-02
2020-01-31 17:49:42 Iteration 1150 Training Loss: 7.647e-02 Loss in Target Net: 2.881e-02
2020-01-31 17:50:04 Iteration 1200 Training Loss: 7.950e-02 Loss in Target Net: 2.095e-02
2020-01-31 17:50:25 Iteration 1250 Training Loss: 7.903e-02 Loss in Target Net: 1.298e-02
2020-01-31 17:50:47 Iteration 1300 Training Loss: 7.983e-02 Loss in Target Net: 2.555e-02
2020-01-31 17:51:08 Iteration 1350 Training Loss: 7.978e-02 Loss in Target Net: 1.670e-02
2020-01-31 17:51:29 Iteration 1400 Training Loss: 7.998e-02 Loss in Target Net: 2.980e-02
2020-01-31 17:51:50 Iteration 1450 Training Loss: 7.496e-02 Loss in Target Net: 2.619e-02
2020-01-31 17:52:11 Iteration 1500 Training Loss: 7.266e-02 Loss in Target Net: 2.965e-02
2020-01-31 17:52:32 Iteration 1550 Training Loss: 7.625e-02 Loss in Target Net: 3.573e-02
2020-01-31 17:52:54 Iteration 1600 Training Loss: 7.181e-02 Loss in Target Net: 3.796e-02
2020-01-31 17:53:16 Iteration 1650 Training Loss: 7.206e-02 Loss in Target Net: 2.376e-02
2020-01-31 17:53:37 Iteration 1700 Training Loss: 7.866e-02 Loss in Target Net: 2.292e-02
2020-01-31 17:53:58 Iteration 1750 Training Loss: 7.978e-02 Loss in Target Net: 2.314e-02
2020-01-31 17:54:20 Iteration 1800 Training Loss: 7.927e-02 Loss in Target Net: 1.990e-02
2020-01-31 17:54:41 Iteration 1850 Training Loss: 7.395e-02 Loss in Target Net: 2.430e-02
2020-01-31 17:55:03 Iteration 1900 Training Loss: 7.998e-02 Loss in Target Net: 2.502e-02
2020-01-31 17:55:24 Iteration 1950 Training Loss: 7.426e-02 Loss in Target Net: 2.660e-02
2020-01-31 17:55:45 Iteration 2000 Training Loss: 7.746e-02 Loss in Target Net: 1.868e-02
2020-01-31 17:56:07 Iteration 2050 Training Loss: 7.288e-02 Loss in Target Net: 3.275e-02
2020-01-31 17:56:28 Iteration 2100 Training Loss: 7.121e-02 Loss in Target Net: 2.678e-02
2020-01-31 17:56:50 Iteration 2150 Training Loss: 7.635e-02 Loss in Target Net: 2.269e-02
2020-01-31 17:57:11 Iteration 2200 Training Loss: 7.961e-02 Loss in Target Net: 1.692e-02
2020-01-31 17:57:32 Iteration 2250 Training Loss: 8.204e-02 Loss in Target Net: 2.113e-02
2020-01-31 17:57:54 Iteration 2300 Training Loss: 7.540e-02 Loss in Target Net: 3.848e-02
2020-01-31 17:58:15 Iteration 2350 Training Loss: 7.625e-02 Loss in Target Net: 2.869e-02
2020-01-31 17:58:37 Iteration 2400 Training Loss: 7.480e-02 Loss in Target Net: 2.541e-02
2020-01-31 17:58:58 Iteration 2450 Training Loss: 7.761e-02 Loss in Target Net: 2.280e-02
2020-01-31 17:59:20 Iteration 2500 Training Loss: 7.759e-02 Loss in Target Net: 2.921e-02
2020-01-31 17:59:41 Iteration 2550 Training Loss: 6.977e-02 Loss in Target Net: 1.683e-02
2020-01-31 18:00:02 Iteration 2600 Training Loss: 7.767e-02 Loss in Target Net: 2.756e-02
2020-01-31 18:00:24 Iteration 2650 Training Loss: 8.007e-02 Loss in Target Net: 3.526e-02
2020-01-31 18:00:45 Iteration 2700 Training Loss: 7.992e-02 Loss in Target Net: 2.248e-02
2020-01-31 18:01:06 Iteration 2750 Training Loss: 7.760e-02 Loss in Target Net: 2.967e-02
2020-01-31 18:01:27 Iteration 2800 Training Loss: 7.647e-02 Loss in Target Net: 2.117e-02
2020-01-31 18:01:49 Iteration 2850 Training Loss: 7.340e-02 Loss in Target Net: 2.338e-02
2020-01-31 18:02:10 Iteration 2900 Training Loss: 8.363e-02 Loss in Target Net: 2.101e-02
2020-01-31 18:02:31 Iteration 2950 Training Loss: 8.243e-02 Loss in Target Net: 2.333e-02
2020-01-31 18:02:52 Iteration 3000 Training Loss: 7.822e-02 Loss in Target Net: 1.996e-02
2020-01-31 18:03:13 Iteration 3050 Training Loss: 7.788e-02 Loss in Target Net: 2.914e-02
2020-01-31 18:03:34 Iteration 3100 Training Loss: 7.405e-02 Loss in Target Net: 2.662e-02
2020-01-31 18:03:56 Iteration 3150 Training Loss: 7.676e-02 Loss in Target Net: 2.111e-02
2020-01-31 18:04:17 Iteration 3200 Training Loss: 7.925e-02 Loss in Target Net: 2.549e-02
2020-01-31 18:04:38 Iteration 3250 Training Loss: 7.295e-02 Loss in Target Net: 2.845e-02
2020-01-31 18:05:00 Iteration 3300 Training Loss: 7.384e-02 Loss in Target Net: 2.259e-02
2020-01-31 18:05:21 Iteration 3350 Training Loss: 7.427e-02 Loss in Target Net: 2.749e-02
2020-01-31 18:05:43 Iteration 3400 Training Loss: 8.134e-02 Loss in Target Net: 2.765e-02
2020-01-31 18:06:04 Iteration 3450 Training Loss: 8.437e-02 Loss in Target Net: 2.262e-02
2020-01-31 18:06:25 Iteration 3500 Training Loss: 7.689e-02 Loss in Target Net: 2.562e-02
2020-01-31 18:06:47 Iteration 3550 Training Loss: 6.961e-02 Loss in Target Net: 2.680e-02
2020-01-31 18:07:08 Iteration 3600 Training Loss: 7.840e-02 Loss in Target Net: 1.975e-02
2020-01-31 18:07:29 Iteration 3650 Training Loss: 7.849e-02 Loss in Target Net: 2.072e-02
2020-01-31 18:07:50 Iteration 3700 Training Loss: 8.189e-02 Loss in Target Net: 2.311e-02
2020-01-31 18:08:13 Iteration 3750 Training Loss: 7.731e-02 Loss in Target Net: 2.695e-02
2020-01-31 18:08:34 Iteration 3800 Training Loss: 7.559e-02 Loss in Target Net: 2.430e-02
2020-01-31 18:08:55 Iteration 3850 Training Loss: 7.513e-02 Loss in Target Net: 2.335e-02
2020-01-31 18:09:17 Iteration 3900 Training Loss: 7.838e-02 Loss in Target Net: 1.826e-02
2020-01-31 18:09:38 Iteration 3950 Training Loss: 7.254e-02 Loss in Target Net: 2.643e-02
2020-01-31 18:10:01 Iteration 3999 Training Loss: 7.259e-02 Loss in Target Net: 2.225e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:10:05, Epoch 0, Iteration 7, loss 0.262 (3.724), acc 94.231 (66.600)

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

TIME ELAPSED (mins): 111

TARGET INDEX: 49

DPN92 0

SENet18 0

ResNet50 1

ResNeXt29_2x64d 0

GoogLeNet 0

MobileNetV2 1

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=5, 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/4000/5

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

2020-01-31 17:41:34 Iteration 0 Training Loss: 1.013e+00 Loss in Target Net: 3.871e-01

2020-01-31 17:41:55 Iteration 50 Training Loss: 1.036e-01 Loss in Target Net: 2.418e-02

2020-01-31 17:42:17 Iteration 100 Training Loss: 8.948e-02 Loss in Target Net: 2.045e-02

2020-01-31 17:42:38 Iteration 150 Training Loss: 8.795e-02 Loss in Target Net: 2.162e-02

2020-01-31 17:42:59 Iteration 200 Training Loss: 9.130e-02 Loss in Target Net: 1.803e-02

2020-01-31 17:43:21 Iteration 250 Training Loss: 8.816e-02 Loss in Target Net: 2.395e-02

2020-01-31 17:43:42 Iteration 300 Training Loss: 8.366e-02 Loss in Target Net: 1.511e-02

2020-01-31 17:44:04 Iteration 350 Training Loss: 7.727e-02 Loss in Target Net: 2.758e-02

2020-01-31 17:44:25 Iteration 400 Training Loss: 8.815e-02 Loss in Target Net: 2.788e-02

2020-01-31 17:44:45 Iteration 450 Training Loss: 7.625e-02 Loss in Target Net: 2.747e-02

2020-01-31 17:45:06 Iteration 500 Training Loss: 7.909e-02 Loss in Target Net: 2.147e-02

2020-01-31 17:45:27 Iteration 550 Training Loss: 7.683e-02 Loss in Target Net: 2.721e-02

2020-01-31 17:45:48 Iteration 600 Training Loss: 8.266e-02 Loss in Target Net: 1.998e-02

2020-01-31 17:46:10 Iteration 650 Training Loss: 7.562e-02 Loss in Target Net: 3.554e-02

2020-01-31 17:46:31 Iteration 700 Training Loss: 7.478e-02 Loss in Target Net: 2.860e-02

2020-01-31 17:46:53 Iteration 750 Training Loss: 7.594e-02 Loss in Target Net: 2.852e-02

2020-01-31 17:47:14 Iteration 800 Training Loss: 8.192e-02 Loss in Target Net: 2.820e-02

2020-01-31 17:47:35 Iteration 850 Training Loss: 7.549e-02 Loss in Target Net: 2.303e-02

2020-01-31 17:47:56 Iteration 900 Training Loss: 7.736e-02 Loss in Target Net: 3.213e-02

2020-01-31 17:48:17 Iteration 950 Training Loss: 7.711e-02 Loss in Target Net: 4.165e-02

2020-01-31 17:48:38 Iteration 1000 Training Loss: 7.549e-02 Loss in Target Net: 2.337e-02

2020-01-31 17:49:00 Iteration 1050 Training Loss: 7.283e-02 Loss in Target Net: 2.736e-02

2020-01-31 17:49:21 Iteration 1100 Training Loss: 8.305e-02 Loss in Target Net: 2.553e-02

2020-01-31 17:49:42 Iteration 1150 Training Loss: 7.647e-02 Loss in Target Net: 2.881e-02

2020-01-31 17:50:04 Iteration 1200 Training Loss: 7.950e-02 Loss in Target Net: 2.095e-02

2020-01-31 17:50:25 Iteration 1250 Training Loss: 7.903e-02 Loss in Target Net: 1.298e-02

2020-01-31 17:50:47 Iteration 1300 Training Loss: 7.983e-02 Loss in Target Net: 2.555e-02

2020-01-31 17:51:08 Iteration 1350 Training Loss: 7.978e-02 Loss in Target Net: 1.670e-02

2020-01-31 17:51:29 Iteration 1400 Training Loss: 7.998e-02 Loss in Target Net: 2.980e-02

2020-01-31 17:51:50 Iteration 1450 Training Loss: 7.496e-02 Loss in Target Net: 2.619e-02

2020-01-31 17:52:11 Iteration 1500 Training Loss: 7.266e-02 Loss in Target Net: 2.965e-02

2020-01-31 17:52:32 Iteration 1550 Training Loss: 7.625e-02 Loss in Target Net: 3.573e-02

2020-01-31 17:52:54 Iteration 1600 Training Loss: 7.181e-02 Loss in Target Net: 3.796e-02

2020-01-31 17:53:16 Iteration 1650 Training Loss: 7.206e-02 Loss in Target Net: 2.376e-02

2020-01-31 17:53:37 Iteration 1700 Training Loss: 7.866e-02 Loss in Target Net: 2.292e-02

2020-01-31 17:53:58 Iteration 1750 Training Loss: 7.978e-02 Loss in Target Net: 2.314e-02

2020-01-31 17:54:20 Iteration 1800 Training Loss: 7.927e-02 Loss in Target Net: 1.990e-02

2020-01-31 17:54:41 Iteration 1850 Training Loss: 7.395e-02 Loss in Target Net: 2.430e-02

2020-01-31 17:55:03 Iteration 1900 Training Loss: 7.998e-02 Loss in Target Net: 2.502e-02

2020-01-31 17:55:24 Iteration 1950 Training Loss: 7.426e-02 Loss in Target Net: 2.660e-02

2020-01-31 17:55:45 Iteration 2000 Training Loss: 7.746e-02 Loss in Target Net: 1.868e-02

2020-01-31 17:56:07 Iteration 2050 Training Loss: 7.288e-02 Loss in Target Net: 3.275e-02

2020-01-31 17:56:28 Iteration 2100 Training Loss: 7.121e-02 Loss in Target Net: 2.678e-02

2020-01-31 17:56:50 Iteration 2150 Training Loss: 7.635e-02 Loss in Target Net: 2.269e-02

2020-01-31 17:57:11 Iteration 2200 Training Loss: 7.961e-02 Loss in Target Net: 1.692e-02

2020-01-31 17:57:32 Iteration 2250 Training Loss: 8.204e-02 Loss in Target Net: 2.113e-02

2020-01-31 17:57:54 Iteration 2300 Training Loss: 7.540e-02 Loss in Target Net: 3.848e-02

2020-01-31 17:58:15 Iteration 2350 Training Loss: 7.625e-02 Loss in Target Net: 2.869e-02

2020-01-31 17:58:37 Iteration 2400 Training Loss: 7.480e-02 Loss in Target Net: 2.541e-02

2020-01-31 17:58:58 Iteration 2450 Training Loss: 7.761e-02 Loss in Target Net: 2.280e-02

2020-01-31 17:59:20 Iteration 2500 Training Loss: 7.759e-02 Loss in Target Net: 2.921e-02

2020-01-31 17:59:41 Iteration 2550 Training Loss: 6.977e-02 Loss in Target Net: 1.683e-02

2020-01-31 18:00:02 Iteration 2600 Training Loss: 7.767e-02 Loss in Target Net: 2.756e-02

2020-01-31 18:00:24 Iteration 2650 Training Loss: 8.007e-02 Loss in Target Net: 3.526e-02

2020-01-31 18:00:45 Iteration 2700 Training Loss: 7.992e-02 Loss in Target Net: 2.248e-02

2020-01-31 18:01:06 Iteration 2750 Training Loss: 7.760e-02 Loss in Target Net: 2.967e-02

2020-01-31 18:01:27 Iteration 2800 Training Loss: 7.647e-02 Loss in Target Net: 2.117e-02

2020-01-31 18:01:49 Iteration 2850 Training Loss: 7.340e-02 Loss in Target Net: 2.338e-02

2020-01-31 18:02:10 Iteration 2900 Training Loss: 8.363e-02 Loss in Target Net: 2.101e-02

2020-01-31 18:02:31 Iteration 2950 Training Loss: 8.243e-02 Loss in Target Net: 2.333e-02

2020-01-31 18:02:52 Iteration 3000 Training Loss: 7.822e-02 Loss in Target Net: 1.996e-02

2020-01-31 18:03:13 Iteration 3050 Training Loss: 7.788e-02 Loss in Target Net: 2.914e-02

2020-01-31 18:03:34 Iteration 3100 Training Loss: 7.405e-02 Loss in Target Net: 2.662e-02

2020-01-31 18:03:56 Iteration 3150 Training Loss: 7.676e-02 Loss in Target Net: 2.111e-02

2020-01-31 18:04:17 Iteration 3200 Training Loss: 7.925e-02 Loss in Target Net: 2.549e-02

2020-01-31 18:04:38 Iteration 3250 Training Loss: 7.295e-02 Loss in Target Net: 2.845e-02

2020-01-31 18:05:00 Iteration 3300 Training Loss: 7.384e-02 Loss in Target Net: 2.259e-02

2020-01-31 18:05:21 Iteration 3350 Training Loss: 7.427e-02 Loss in Target Net: 2.749e-02

2020-01-31 18:05:43 Iteration 3400 Training Loss: 8.134e-02 Loss in Target Net: 2.765e-02

2020-01-31 18:06:04 Iteration 3450 Training Loss: 8.437e-02 Loss in Target Net: 2.262e-02

2020-01-31 18:06:25 Iteration 3500 Training Loss: 7.689e-02 Loss in Target Net: 2.562e-02

2020-01-31 18:06:47 Iteration 3550 Training Loss: 6.961e-02 Loss in Target Net: 2.680e-02

2020-01-31 18:07:08 Iteration 3600 Training Loss: 7.840e-02 Loss in Target Net: 1.975e-02

2020-01-31 18:07:29 Iteration 3650 Training Loss: 7.849e-02 Loss in Target Net: 2.072e-02

2020-01-31 18:07:50 Iteration 3700 Training Loss: 8.189e-02 Loss in Target Net: 2.311e-02

2020-01-31 18:08:13 Iteration 3750 Training Loss: 7.731e-02 Loss in Target Net: 2.695e-02

2020-01-31 18:08:34 Iteration 3800 Training Loss: 7.559e-02 Loss in Target Net: 2.430e-02

2020-01-31 18:08:55 Iteration 3850 Training Loss: 7.513e-02 Loss in Target Net: 2.335e-02

2020-01-31 18:09:17 Iteration 3900 Training Loss: 7.838e-02 Loss in Target Net: 1.826e-02

2020-01-31 18:09:38 Iteration 3950 Training Loss: 7.254e-02 Loss in Target Net: 2.643e-02

2020-01-31 18:10:01 Iteration 3999 Training Loss: 7.259e-02 Loss in Target Net: 2.225e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-01-31 18:10:05, Epoch 0, Iteration 7, loss 0.262 (3.724), acc 94.231 (66.600)