SunayBhat1/PureGen_Poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
------SUMMARY------
TIME ELAPSED (mins): 29
TARGET INDEX: 26
DPN92 0
SENet18 0
ResNet50 0
ResNeXt29_2x64d 0
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='3', 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=27, 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/4000/27
Selected base image indices: [213, 225, 227, 247, 249]
2020-01-31 20:11:12 Iteration 0 Training Loss: 1.030e+00 Loss in Target Net: 3.789e-01
2020-01-31 20:11:33 Iteration 50 Training Loss: 9.271e-02 Loss in Target Net: 3.921e-02
2020-01-31 20:11:55 Iteration 100 Training Loss: 8.129e-02 Loss in Target Net: 3.508e-02
2020-01-31 20:12:17 Iteration 150 Training Loss: 7.572e-02 Loss in Target Net: 3.060e-02
2020-01-31 20:12:38 Iteration 200 Training Loss: 8.299e-02 Loss in Target Net: 2.458e-02
2020-01-31 20:13:01 Iteration 250 Training Loss: 8.086e-02 Loss in Target Net: 2.300e-02
2020-01-31 20:13:24 Iteration 300 Training Loss: 7.646e-02 Loss in Target Net: 2.980e-02
2020-01-31 20:13:45 Iteration 350 Training Loss: 7.267e-02 Loss in Target Net: 2.725e-02
2020-01-31 20:14:07 Iteration 400 Training Loss: 7.537e-02 Loss in Target Net: 2.506e-02
2020-01-31 20:14:29 Iteration 450 Training Loss: 7.075e-02 Loss in Target Net: 2.552e-02
2020-01-31 20:14:51 Iteration 500 Training Loss: 7.865e-02 Loss in Target Net: 1.797e-02
2020-01-31 20:15:14 Iteration 550 Training Loss: 7.214e-02 Loss in Target Net: 2.746e-02
2020-01-31 20:15:34 Iteration 600 Training Loss: 6.997e-02 Loss in Target Net: 3.034e-02
2020-01-31 20:15:54 Iteration 650 Training Loss: 7.242e-02 Loss in Target Net: 3.018e-02
2020-01-31 20:16:13 Iteration 700 Training Loss: 7.615e-02 Loss in Target Net: 2.355e-02
2020-01-31 20:16:33 Iteration 750 Training Loss: 7.864e-02 Loss in Target Net: 2.735e-02
2020-01-31 20:16:53 Iteration 800 Training Loss: 7.187e-02 Loss in Target Net: 2.622e-02
2020-01-31 20:17:12 Iteration 850 Training Loss: 7.649e-02 Loss in Target Net: 3.248e-02
2020-01-31 20:17:32 Iteration 900 Training Loss: 7.225e-02 Loss in Target Net: 2.747e-02
2020-01-31 20:17:53 Iteration 950 Training Loss: 7.193e-02 Loss in Target Net: 3.179e-02
2020-01-31 20:18:13 Iteration 1000 Training Loss: 6.923e-02 Loss in Target Net: 3.429e-02
2020-01-31 20:18:34 Iteration 1050 Training Loss: 7.048e-02 Loss in Target Net: 2.789e-02
2020-01-31 20:18:54 Iteration 1100 Training Loss: 8.182e-02 Loss in Target Net: 2.207e-02
2020-01-31 20:19:15 Iteration 1150 Training Loss: 6.989e-02 Loss in Target Net: 3.723e-02
2020-01-31 20:19:36 Iteration 1200 Training Loss: 7.333e-02 Loss in Target Net: 3.512e-02
2020-01-31 20:19:55 Iteration 1250 Training Loss: 7.399e-02 Loss in Target Net: 2.810e-02
2020-01-31 20:20:15 Iteration 1300 Training Loss: 8.047e-02 Loss in Target Net: 2.018e-02
2020-01-31 20:20:34 Iteration 1350 Training Loss: 6.985e-02 Loss in Target Net: 1.748e-02
2020-01-31 20:20:54 Iteration 1400 Training Loss: 6.831e-02 Loss in Target Net: 1.818e-02
2020-01-31 20:21:14 Iteration 1450 Training Loss: 7.760e-02 Loss in Target Net: 2.663e-02
2020-01-31 20:21:33 Iteration 1500 Training Loss: 6.870e-02 Loss in Target Net: 1.905e-02
2020-01-31 20:21:53 Iteration 1550 Training Loss: 6.912e-02 Loss in Target Net: 2.026e-02
2020-01-31 20:22:13 Iteration 1600 Training Loss: 7.793e-02 Loss in Target Net: 2.172e-02
2020-01-31 20:22:33 Iteration 1650 Training Loss: 7.116e-02 Loss in Target Net: 1.986e-02
2020-01-31 20:22:53 Iteration 1700 Training Loss: 7.238e-02 Loss in Target Net: 2.382e-02
2020-01-31 20:23:14 Iteration 1750 Training Loss: 7.002e-02 Loss in Target Net: 2.591e-02
2020-01-31 20:23:35 Iteration 1800 Training Loss: 7.817e-02 Loss in Target Net: 2.526e-02
2020-01-31 20:23:56 Iteration 1850 Training Loss: 7.194e-02 Loss in Target Net: 2.153e-02
2020-01-31 20:24:17 Iteration 1900 Training Loss: 7.283e-02 Loss in Target Net: 3.206e-02
2020-01-31 20:24:40 Iteration 1950 Training Loss: 6.579e-02 Loss in Target Net: 2.462e-02
2020-01-31 20:25:02 Iteration 2000 Training Loss: 6.903e-02 Loss in Target Net: 2.493e-02
2020-01-31 20:25:24 Iteration 2050 Training Loss: 7.186e-02 Loss in Target Net: 3.303e-02
2020-01-31 20:25:46 Iteration 2100 Training Loss: 7.253e-02 Loss in Target Net: 2.410e-02
2020-01-31 20:26:09 Iteration 2150 Training Loss: 6.857e-02 Loss in Target Net: 2.295e-02
2020-01-31 20:26:32 Iteration 2200 Training Loss: 7.628e-02 Loss in Target Net: 2.838e-02
2020-01-31 20:26:54 Iteration 2250 Training Loss: 7.056e-02 Loss in Target Net: 2.250e-02
2020-01-31 20:27:16 Iteration 2300 Training Loss: 7.659e-02 Loss in Target Net: 2.946e-02
2020-01-31 20:27:39 Iteration 2350 Training Loss: 7.199e-02 Loss in Target Net: 3.008e-02
2020-01-31 20:28:02 Iteration 2400 Training Loss: 7.788e-02 Loss in Target Net: 1.717e-02
2020-01-31 20:28:25 Iteration 2450 Training Loss: 7.663e-02 Loss in Target Net: 2.527e-02
2020-01-31 20:28:47 Iteration 2500 Training Loss: 7.188e-02 Loss in Target Net: 1.861e-02
2020-01-31 20:29:10 Iteration 2550 Training Loss: 7.173e-02 Loss in Target Net: 2.583e-02
2020-01-31 20:29:32 Iteration 2600 Training Loss: 7.846e-02 Loss in Target Net: 2.073e-02
2020-01-31 20:29:54 Iteration 2650 Training Loss: 7.507e-02 Loss in Target Net: 2.017e-02
2020-01-31 20:30:17 Iteration 2700 Training Loss: 7.812e-02 Loss in Target Net: 3.055e-02
2020-01-31 20:30:40 Iteration 2750 Training Loss: 7.479e-02 Loss in Target Net: 2.744e-02
2020-01-31 20:31:02 Iteration 2800 Training Loss: 7.240e-02 Loss in Target Net: 2.573e-02
2020-01-31 20:31:24 Iteration 2850 Training Loss: 7.066e-02 Loss in Target Net: 2.423e-02
2020-01-31 20:31:46 Iteration 2900 Training Loss: 7.580e-02 Loss in Target Net: 2.357e-02
2020-01-31 20:32:08 Iteration 2950 Training Loss: 7.294e-02 Loss in Target Net: 2.478e-02
2020-01-31 20:32:30 Iteration 3000 Training Loss: 7.498e-02 Loss in Target Net: 2.145e-02
2020-01-31 20:32:52 Iteration 3050 Training Loss: 7.377e-02 Loss in Target Net: 2.367e-02
2020-01-31 20:33:15 Iteration 3100 Training Loss: 7.785e-02 Loss in Target Net: 2.482e-02
2020-01-31 20:33:37 Iteration 3150 Training Loss: 7.590e-02 Loss in Target Net: 2.357e-02
2020-01-31 20:33:58 Iteration 3200 Training Loss: 6.872e-02 Loss in Target Net: 2.764e-02
2020-01-31 20:34:21 Iteration 3250 Training Loss: 7.327e-02 Loss in Target Net: 2.660e-02
2020-01-31 20:34:43 Iteration 3300 Training Loss: 7.059e-02 Loss in Target Net: 1.943e-02
2020-01-31 20:35:06 Iteration 3350 Training Loss: 7.223e-02 Loss in Target Net: 2.708e-02
2020-01-31 20:35:28 Iteration 3400 Training Loss: 7.084e-02 Loss in Target Net: 1.988e-02
2020-01-31 20:35:50 Iteration 3450 Training Loss: 7.701e-02 Loss in Target Net: 2.948e-02
2020-01-31 20:36:12 Iteration 3500 Training Loss: 6.915e-02 Loss in Target Net: 2.114e-02
2020-01-31 20:36:35 Iteration 3550 Training Loss: 7.196e-02 Loss in Target Net: 1.871e-02
2020-01-31 20:36:58 Iteration 3600 Training Loss: 7.138e-02 Loss in Target Net: 2.756e-02
2020-01-31 20:37:20 Iteration 3650 Training Loss: 7.423e-02 Loss in Target Net: 2.152e-02
2020-01-31 20:37:42 Iteration 3700 Training Loss: 7.521e-02 Loss in Target Net: 1.960e-02
2020-01-31 20:38:04 Iteration 3750 Training Loss: 7.745e-02 Loss in Target Net: 2.160e-02
2020-01-31 20:38:26 Iteration 3800 Training Loss: 7.215e-02 Loss in Target Net: 1.558e-02
2020-01-31 20:38:48 Iteration 3850 Training Loss: 7.288e-02 Loss in Target Net: 1.154e-02
2020-01-31 20:39:10 Iteration 3900 Training Loss: 7.619e-02 Loss in Target Net: 1.940e-02
2020-01-31 20:39:32 Iteration 3950 Training Loss: 7.665e-02 Loss in Target Net: 2.119e-02
2020-01-31 20:39:54 Iteration 3999 Training Loss: 7.204e-02 Loss in Target Net: 2.410e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-01-31 20:39:58, Epoch 0, Iteration 7, loss 2.131 (4.220), acc 86.538 (69.400)

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

TIME ELAPSED (mins): 29

TARGET INDEX: 26

DPN92 0

SENet18 0

ResNet50 0

ResNeXt29_2x64d 0

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='3', 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=27, 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/4000/27

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

2020-01-31 20:11:12 Iteration 0 Training Loss: 1.030e+00 Loss in Target Net: 3.789e-01

2020-01-31 20:11:33 Iteration 50 Training Loss: 9.271e-02 Loss in Target Net: 3.921e-02

2020-01-31 20:11:55 Iteration 100 Training Loss: 8.129e-02 Loss in Target Net: 3.508e-02

2020-01-31 20:12:17 Iteration 150 Training Loss: 7.572e-02 Loss in Target Net: 3.060e-02

2020-01-31 20:12:38 Iteration 200 Training Loss: 8.299e-02 Loss in Target Net: 2.458e-02

2020-01-31 20:13:01 Iteration 250 Training Loss: 8.086e-02 Loss in Target Net: 2.300e-02

2020-01-31 20:13:24 Iteration 300 Training Loss: 7.646e-02 Loss in Target Net: 2.980e-02

2020-01-31 20:13:45 Iteration 350 Training Loss: 7.267e-02 Loss in Target Net: 2.725e-02

2020-01-31 20:14:07 Iteration 400 Training Loss: 7.537e-02 Loss in Target Net: 2.506e-02

2020-01-31 20:14:29 Iteration 450 Training Loss: 7.075e-02 Loss in Target Net: 2.552e-02

2020-01-31 20:14:51 Iteration 500 Training Loss: 7.865e-02 Loss in Target Net: 1.797e-02

2020-01-31 20:15:14 Iteration 550 Training Loss: 7.214e-02 Loss in Target Net: 2.746e-02

2020-01-31 20:15:34 Iteration 600 Training Loss: 6.997e-02 Loss in Target Net: 3.034e-02

2020-01-31 20:15:54 Iteration 650 Training Loss: 7.242e-02 Loss in Target Net: 3.018e-02

2020-01-31 20:16:13 Iteration 700 Training Loss: 7.615e-02 Loss in Target Net: 2.355e-02

2020-01-31 20:16:33 Iteration 750 Training Loss: 7.864e-02 Loss in Target Net: 2.735e-02

2020-01-31 20:16:53 Iteration 800 Training Loss: 7.187e-02 Loss in Target Net: 2.622e-02

2020-01-31 20:17:12 Iteration 850 Training Loss: 7.649e-02 Loss in Target Net: 3.248e-02

2020-01-31 20:17:32 Iteration 900 Training Loss: 7.225e-02 Loss in Target Net: 2.747e-02

2020-01-31 20:17:53 Iteration 950 Training Loss: 7.193e-02 Loss in Target Net: 3.179e-02

2020-01-31 20:18:13 Iteration 1000 Training Loss: 6.923e-02 Loss in Target Net: 3.429e-02

2020-01-31 20:18:34 Iteration 1050 Training Loss: 7.048e-02 Loss in Target Net: 2.789e-02

2020-01-31 20:18:54 Iteration 1100 Training Loss: 8.182e-02 Loss in Target Net: 2.207e-02

2020-01-31 20:19:15 Iteration 1150 Training Loss: 6.989e-02 Loss in Target Net: 3.723e-02

2020-01-31 20:19:36 Iteration 1200 Training Loss: 7.333e-02 Loss in Target Net: 3.512e-02

2020-01-31 20:19:55 Iteration 1250 Training Loss: 7.399e-02 Loss in Target Net: 2.810e-02

2020-01-31 20:20:15 Iteration 1300 Training Loss: 8.047e-02 Loss in Target Net: 2.018e-02

2020-01-31 20:20:34 Iteration 1350 Training Loss: 6.985e-02 Loss in Target Net: 1.748e-02

2020-01-31 20:20:54 Iteration 1400 Training Loss: 6.831e-02 Loss in Target Net: 1.818e-02

2020-01-31 20:21:14 Iteration 1450 Training Loss: 7.760e-02 Loss in Target Net: 2.663e-02

2020-01-31 20:21:33 Iteration 1500 Training Loss: 6.870e-02 Loss in Target Net: 1.905e-02

2020-01-31 20:21:53 Iteration 1550 Training Loss: 6.912e-02 Loss in Target Net: 2.026e-02

2020-01-31 20:22:13 Iteration 1600 Training Loss: 7.793e-02 Loss in Target Net: 2.172e-02

2020-01-31 20:22:33 Iteration 1650 Training Loss: 7.116e-02 Loss in Target Net: 1.986e-02

2020-01-31 20:22:53 Iteration 1700 Training Loss: 7.238e-02 Loss in Target Net: 2.382e-02

2020-01-31 20:23:14 Iteration 1750 Training Loss: 7.002e-02 Loss in Target Net: 2.591e-02

2020-01-31 20:23:35 Iteration 1800 Training Loss: 7.817e-02 Loss in Target Net: 2.526e-02

2020-01-31 20:23:56 Iteration 1850 Training Loss: 7.194e-02 Loss in Target Net: 2.153e-02

2020-01-31 20:24:17 Iteration 1900 Training Loss: 7.283e-02 Loss in Target Net: 3.206e-02

2020-01-31 20:24:40 Iteration 1950 Training Loss: 6.579e-02 Loss in Target Net: 2.462e-02

2020-01-31 20:25:02 Iteration 2000 Training Loss: 6.903e-02 Loss in Target Net: 2.493e-02

2020-01-31 20:25:24 Iteration 2050 Training Loss: 7.186e-02 Loss in Target Net: 3.303e-02

2020-01-31 20:25:46 Iteration 2100 Training Loss: 7.253e-02 Loss in Target Net: 2.410e-02

2020-01-31 20:26:09 Iteration 2150 Training Loss: 6.857e-02 Loss in Target Net: 2.295e-02

2020-01-31 20:26:32 Iteration 2200 Training Loss: 7.628e-02 Loss in Target Net: 2.838e-02

2020-01-31 20:26:54 Iteration 2250 Training Loss: 7.056e-02 Loss in Target Net: 2.250e-02

2020-01-31 20:27:16 Iteration 2300 Training Loss: 7.659e-02 Loss in Target Net: 2.946e-02

2020-01-31 20:27:39 Iteration 2350 Training Loss: 7.199e-02 Loss in Target Net: 3.008e-02

2020-01-31 20:28:02 Iteration 2400 Training Loss: 7.788e-02 Loss in Target Net: 1.717e-02

2020-01-31 20:28:25 Iteration 2450 Training Loss: 7.663e-02 Loss in Target Net: 2.527e-02

2020-01-31 20:28:47 Iteration 2500 Training Loss: 7.188e-02 Loss in Target Net: 1.861e-02

2020-01-31 20:29:10 Iteration 2550 Training Loss: 7.173e-02 Loss in Target Net: 2.583e-02

2020-01-31 20:29:32 Iteration 2600 Training Loss: 7.846e-02 Loss in Target Net: 2.073e-02

2020-01-31 20:29:54 Iteration 2650 Training Loss: 7.507e-02 Loss in Target Net: 2.017e-02

2020-01-31 20:30:17 Iteration 2700 Training Loss: 7.812e-02 Loss in Target Net: 3.055e-02

2020-01-31 20:30:40 Iteration 2750 Training Loss: 7.479e-02 Loss in Target Net: 2.744e-02

2020-01-31 20:31:02 Iteration 2800 Training Loss: 7.240e-02 Loss in Target Net: 2.573e-02

2020-01-31 20:31:24 Iteration 2850 Training Loss: 7.066e-02 Loss in Target Net: 2.423e-02

2020-01-31 20:31:46 Iteration 2900 Training Loss: 7.580e-02 Loss in Target Net: 2.357e-02

2020-01-31 20:32:08 Iteration 2950 Training Loss: 7.294e-02 Loss in Target Net: 2.478e-02

2020-01-31 20:32:30 Iteration 3000 Training Loss: 7.498e-02 Loss in Target Net: 2.145e-02

2020-01-31 20:32:52 Iteration 3050 Training Loss: 7.377e-02 Loss in Target Net: 2.367e-02

2020-01-31 20:33:15 Iteration 3100 Training Loss: 7.785e-02 Loss in Target Net: 2.482e-02

2020-01-31 20:33:37 Iteration 3150 Training Loss: 7.590e-02 Loss in Target Net: 2.357e-02

2020-01-31 20:33:58 Iteration 3200 Training Loss: 6.872e-02 Loss in Target Net: 2.764e-02

2020-01-31 20:34:21 Iteration 3250 Training Loss: 7.327e-02 Loss in Target Net: 2.660e-02

2020-01-31 20:34:43 Iteration 3300 Training Loss: 7.059e-02 Loss in Target Net: 1.943e-02

2020-01-31 20:35:06 Iteration 3350 Training Loss: 7.223e-02 Loss in Target Net: 2.708e-02

2020-01-31 20:35:28 Iteration 3400 Training Loss: 7.084e-02 Loss in Target Net: 1.988e-02

2020-01-31 20:35:50 Iteration 3450 Training Loss: 7.701e-02 Loss in Target Net: 2.948e-02

2020-01-31 20:36:12 Iteration 3500 Training Loss: 6.915e-02 Loss in Target Net: 2.114e-02

2020-01-31 20:36:35 Iteration 3550 Training Loss: 7.196e-02 Loss in Target Net: 1.871e-02

2020-01-31 20:36:58 Iteration 3600 Training Loss: 7.138e-02 Loss in Target Net: 2.756e-02

2020-01-31 20:37:20 Iteration 3650 Training Loss: 7.423e-02 Loss in Target Net: 2.152e-02

2020-01-31 20:37:42 Iteration 3700 Training Loss: 7.521e-02 Loss in Target Net: 1.960e-02

2020-01-31 20:38:04 Iteration 3750 Training Loss: 7.745e-02 Loss in Target Net: 2.160e-02

2020-01-31 20:38:26 Iteration 3800 Training Loss: 7.215e-02 Loss in Target Net: 1.558e-02

2020-01-31 20:38:48 Iteration 3850 Training Loss: 7.288e-02 Loss in Target Net: 1.154e-02

2020-01-31 20:39:10 Iteration 3900 Training Loss: 7.619e-02 Loss in Target Net: 1.940e-02

2020-01-31 20:39:32 Iteration 3950 Training Loss: 7.665e-02 Loss in Target Net: 2.119e-02

2020-01-31 20:39:54 Iteration 3999 Training Loss: 7.204e-02 Loss in Target Net: 2.410e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-01-31 20:39:58, Epoch 0, Iteration 7, loss 2.131 (4.220), acc 86.538 (69.400)