SunayBhat1/puregen-poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=0, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean/1500/0
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 10:49:06 Iteration 0 Training Loss: 9.932e-01 Loss in Target Net: 1.365e+00
2020-02-02 10:49:25 Iteration 50 Training Loss: 2.672e-01 Loss in Target Net: 1.062e-01
2020-02-02 10:49:41 Iteration 100 Training Loss: 2.459e-01 Loss in Target Net: 9.781e-02
2020-02-02 10:49:58 Iteration 150 Training Loss: 2.277e-01 Loss in Target Net: 7.572e-02
2020-02-02 10:50:16 Iteration 200 Training Loss: 2.210e-01 Loss in Target Net: 6.241e-02
2020-02-02 10:50:34 Iteration 250 Training Loss: 2.113e-01 Loss in Target Net: 6.134e-02
2020-02-02 10:50:52 Iteration 300 Training Loss: 2.103e-01 Loss in Target Net: 7.168e-02
2020-02-02 10:51:09 Iteration 350 Training Loss: 2.121e-01 Loss in Target Net: 4.938e-02
2020-02-02 10:51:29 Iteration 400 Training Loss: 2.104e-01 Loss in Target Net: 4.228e-02
2020-02-02 10:51:45 Iteration 450 Training Loss: 2.025e-01 Loss in Target Net: 3.842e-02
2020-02-02 10:52:03 Iteration 500 Training Loss: 2.051e-01 Loss in Target Net: 4.905e-02
2020-02-02 10:52:20 Iteration 550 Training Loss: 1.976e-01 Loss in Target Net: 5.025e-02
2020-02-02 10:52:39 Iteration 600 Training Loss: 2.111e-01 Loss in Target Net: 4.175e-02
2020-02-02 10:52:57 Iteration 650 Training Loss: 1.974e-01 Loss in Target Net: 3.466e-02
2020-02-02 10:53:15 Iteration 700 Training Loss: 1.957e-01 Loss in Target Net: 4.145e-02
2020-02-02 10:53:33 Iteration 750 Training Loss: 2.002e-01 Loss in Target Net: 3.955e-02
2020-02-02 10:53:50 Iteration 800 Training Loss: 2.003e-01 Loss in Target Net: 3.796e-02
2020-02-02 10:54:08 Iteration 850 Training Loss: 2.109e-01 Loss in Target Net: 3.978e-02
2020-02-02 10:54:25 Iteration 900 Training Loss: 1.965e-01 Loss in Target Net: 4.174e-02
2020-02-02 10:54:44 Iteration 950 Training Loss: 1.954e-01 Loss in Target Net: 3.093e-02
2020-02-02 10:55:03 Iteration 1000 Training Loss: 1.964e-01 Loss in Target Net: 3.952e-02
2020-02-02 10:55:20 Iteration 1050 Training Loss: 1.942e-01 Loss in Target Net: 3.774e-02
2020-02-02 10:55:37 Iteration 1100 Training Loss: 1.927e-01 Loss in Target Net: 3.763e-02
2020-02-02 10:55:55 Iteration 1150 Training Loss: 1.952e-01 Loss in Target Net: 3.500e-02
2020-02-02 10:56:12 Iteration 1200 Training Loss: 1.923e-01 Loss in Target Net: 4.845e-02
2020-02-02 10:56:29 Iteration 1250 Training Loss: 1.954e-01 Loss in Target Net: 3.993e-02
2020-02-02 10:56:49 Iteration 1300 Training Loss: 1.957e-01 Loss in Target Net: 3.858e-02
2020-02-02 10:57:09 Iteration 1350 Training Loss: 1.982e-01 Loss in Target Net: 3.769e-02
2020-02-02 10:57:27 Iteration 1400 Training Loss: 1.953e-01 Loss in Target Net: 3.836e-02
2020-02-02 10:57:45 Iteration 1450 Training Loss: 1.938e-01 Loss in Target Net: 4.044e-02
2020-02-02 10:58:02 Iteration 1499 Training Loss: 1.963e-01 Loss in Target Net: 3.991e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-02 10:58:11, Epoch 0, Iteration 7, loss 0.257 (0.478), acc 92.308 (90.200)
2020-02-02 10:59:09, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.8269538, -2.2299647, -1.0302503, -0.6328676, 0.37673587, 0.6563255, 7.2333856, -2.5393019, 2.360554, -2.0871308], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-02 11:00:09 Epoch 59, Val iteration 0, acc 92.400 (92.400)
2020-02-02 11:00:17 Epoch 59, Val iteration 19, acc 93.000 (92.890)
* Prec: 92.8900016784668
--------
------SUMMARY------
TIME ELAPSED (mins): 9
TARGET INDEX: 0
DPN92 0
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=1, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean/1500/1
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 05:57:11 Iteration 0 Training Loss: 1.031e+00 Loss in Target Net: 1.402e+00
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=1, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean/1500/1
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 10:49:16 Iteration 0 Training Loss: 1.017e+00 Loss in Target Net: 1.328e+00
2020-02-02 10:49:35 Iteration 50 Training Loss: 2.569e-01 Loss in Target Net: 4.221e-02
2020-02-02 10:49:54 Iteration 100 Training Loss: 2.346e-01 Loss in Target Net: 3.162e-02
2020-02-02 10:50:12 Iteration 150 Training Loss: 2.274e-01 Loss in Target Net: 3.487e-02
2020-02-02 10:50:30 Iteration 200 Training Loss: 2.169e-01 Loss in Target Net: 3.115e-02
2020-02-02 10:50:49 Iteration 250 Training Loss: 2.141e-01 Loss in Target Net: 3.251e-02
2020-02-02 10:51:07 Iteration 300 Training Loss: 2.073e-01 Loss in Target Net: 3.120e-02
2020-02-02 10:51:26 Iteration 350 Training Loss: 2.065e-01 Loss in Target Net: 3.040e-02
2020-02-02 10:51:44 Iteration 400 Training Loss: 2.051e-01 Loss in Target Net: 2.931e-02
2020-02-02 10:52:03 Iteration 450 Training Loss: 2.148e-01 Loss in Target Net: 3.760e-02
2020-02-02 10:52:22 Iteration 500 Training Loss: 2.039e-01 Loss in Target Net: 3.151e-02
2020-02-02 10:52:41 Iteration 550 Training Loss: 1.998e-01 Loss in Target Net: 3.428e-02
2020-02-02 10:53:00 Iteration 600 Training Loss: 2.014e-01 Loss in Target Net: 2.809e-02
2020-02-02 10:53:20 Iteration 650 Training Loss: 2.010e-01 Loss in Target Net: 3.119e-02
2020-02-02 10:53:39 Iteration 700 Training Loss: 2.025e-01 Loss in Target Net: 2.974e-02
2020-02-02 10:53:58 Iteration 750 Training Loss: 1.952e-01 Loss in Target Net: 2.999e-02
2020-02-02 10:54:18 Iteration 800 Training Loss: 1.988e-01 Loss in Target Net: 3.035e-02
2020-02-02 10:54:37 Iteration 850 Training Loss: 1.942e-01 Loss in Target Net: 3.124e-02
2020-02-02 10:54:56 Iteration 900 Training Loss: 1.988e-01 Loss in Target Net: 3.061e-02
2020-02-02 10:55:15 Iteration 950 Training Loss: 1.903e-01 Loss in Target Net: 2.537e-02
2020-02-02 10:55:34 Iteration 1000 Training Loss: 1.966e-01 Loss in Target Net: 2.304e-02
2020-02-02 10:55:53 Iteration 1050 Training Loss: 1.962e-01 Loss in Target Net: 2.530e-02
2020-02-02 10:56:12 Iteration 1100 Training Loss: 1.994e-01 Loss in Target Net: 2.777e-02
2020-02-02 10:56:30 Iteration 1150 Training Loss: 1.919e-01 Loss in Target Net: 2.935e-02
2020-02-02 10:56:48 Iteration 1200 Training Loss: 1.974e-01 Loss in Target Net: 2.385e-02
2020-02-02 10:57:08 Iteration 1250 Training Loss: 1.910e-01 Loss in Target Net: 2.582e-02
2020-02-02 10:57:27 Iteration 1300 Training Loss: 1.939e-01 Loss in Target Net: 2.335e-02
2020-02-02 10:57:47 Iteration 1350 Training Loss: 1.942e-01 Loss in Target Net: 2.602e-02
2020-02-02 10:58:09 Iteration 1400 Training Loss: 1.933e-01 Loss in Target Net: 3.487e-02
2020-02-02 10:58:29 Iteration 1450 Training Loss: 1.913e-01 Loss in Target Net: 2.352e-02
2020-02-02 10:58:49 Iteration 1499 Training Loss: 1.913e-01 Loss in Target Net: 3.005e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-02 10:58:58, Epoch 0, Iteration 7, loss 0.397 (0.413), acc 88.462 (91.400)
2020-02-02 10:59:57, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.3199525, -1.2360389, -3.0512056, 0.6286387, -1.0786347, -3.1990297, 6.2378592, -1.7196736, 5.9849772, -0.9120336], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-02 11:00:57 Epoch 59, Val iteration 0, acc 92.200 (92.200)
2020-02-02 11:01:04 Epoch 59, Val iteration 19, acc 91.200 (92.860)
* Prec: 92.86000175476075
--------
------SUMMARY------
TIME ELAPSED (mins): 9

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=0, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')

Path: chk-black-end2end/mean/1500/0

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

2020-02-02 10:49:06 Iteration 0 Training Loss: 9.932e-01 Loss in Target Net: 1.365e+00

2020-02-02 10:49:25 Iteration 50 Training Loss: 2.672e-01 Loss in Target Net: 1.062e-01

2020-02-02 10:49:41 Iteration 100 Training Loss: 2.459e-01 Loss in Target Net: 9.781e-02

2020-02-02 10:49:58 Iteration 150 Training Loss: 2.277e-01 Loss in Target Net: 7.572e-02

2020-02-02 10:50:16 Iteration 200 Training Loss: 2.210e-01 Loss in Target Net: 6.241e-02

2020-02-02 10:50:34 Iteration 250 Training Loss: 2.113e-01 Loss in Target Net: 6.134e-02

2020-02-02 10:50:52 Iteration 300 Training Loss: 2.103e-01 Loss in Target Net: 7.168e-02

2020-02-02 10:51:09 Iteration 350 Training Loss: 2.121e-01 Loss in Target Net: 4.938e-02

2020-02-02 10:51:29 Iteration 400 Training Loss: 2.104e-01 Loss in Target Net: 4.228e-02

2020-02-02 10:51:45 Iteration 450 Training Loss: 2.025e-01 Loss in Target Net: 3.842e-02

2020-02-02 10:52:03 Iteration 500 Training Loss: 2.051e-01 Loss in Target Net: 4.905e-02

2020-02-02 10:52:20 Iteration 550 Training Loss: 1.976e-01 Loss in Target Net: 5.025e-02

2020-02-02 10:52:39 Iteration 600 Training Loss: 2.111e-01 Loss in Target Net: 4.175e-02

2020-02-02 10:52:57 Iteration 650 Training Loss: 1.974e-01 Loss in Target Net: 3.466e-02

2020-02-02 10:53:15 Iteration 700 Training Loss: 1.957e-01 Loss in Target Net: 4.145e-02

2020-02-02 10:53:33 Iteration 750 Training Loss: 2.002e-01 Loss in Target Net: 3.955e-02

2020-02-02 10:53:50 Iteration 800 Training Loss: 2.003e-01 Loss in Target Net: 3.796e-02

2020-02-02 10:54:08 Iteration 850 Training Loss: 2.109e-01 Loss in Target Net: 3.978e-02

2020-02-02 10:54:25 Iteration 900 Training Loss: 1.965e-01 Loss in Target Net: 4.174e-02

2020-02-02 10:54:44 Iteration 950 Training Loss: 1.954e-01 Loss in Target Net: 3.093e-02

2020-02-02 10:55:03 Iteration 1000 Training Loss: 1.964e-01 Loss in Target Net: 3.952e-02

2020-02-02 10:55:20 Iteration 1050 Training Loss: 1.942e-01 Loss in Target Net: 3.774e-02

2020-02-02 10:55:37 Iteration 1100 Training Loss: 1.927e-01 Loss in Target Net: 3.763e-02

2020-02-02 10:55:55 Iteration 1150 Training Loss: 1.952e-01 Loss in Target Net: 3.500e-02

2020-02-02 10:56:12 Iteration 1200 Training Loss: 1.923e-01 Loss in Target Net: 4.845e-02

2020-02-02 10:56:29 Iteration 1250 Training Loss: 1.954e-01 Loss in Target Net: 3.993e-02

2020-02-02 10:56:49 Iteration 1300 Training Loss: 1.957e-01 Loss in Target Net: 3.858e-02

2020-02-02 10:57:09 Iteration 1350 Training Loss: 1.982e-01 Loss in Target Net: 3.769e-02

2020-02-02 10:57:27 Iteration 1400 Training Loss: 1.953e-01 Loss in Target Net: 3.836e-02

2020-02-02 10:57:45 Iteration 1450 Training Loss: 1.938e-01 Loss in Target Net: 4.044e-02

2020-02-02 10:58:02 Iteration 1499 Training Loss: 1.963e-01 Loss in Target Net: 3.991e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-02 10:58:11, Epoch 0, Iteration 7, loss 0.257 (0.478), acc 92.308 (90.200)

2020-02-02 10:59:09, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.8269538, -2.2299647, -1.0302503, -0.6328676, 0.37673587, 0.6563255, 7.2333856, -2.5393019, 2.360554, -2.0871308], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-02 11:00:09 Epoch 59, Val iteration 0, acc 92.400 (92.400)

2020-02-02 11:00:17 Epoch 59, Val iteration 19, acc 93.000 (92.890)

* Prec: 92.8900016784668

--------

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

TIME ELAPSED (mins): 9

TARGET INDEX: 0

DPN92 0

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=1, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')

Path: chk-black-end2end/mean/1500/1

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

2020-02-02 05:57:11 Iteration 0 Training Loss: 1.031e+00 Loss in Target Net: 1.402e+00

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=1, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')

Path: chk-black-end2end/mean/1500/1

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

2020-02-02 10:49:16 Iteration 0 Training Loss: 1.017e+00 Loss in Target Net: 1.328e+00

2020-02-02 10:49:35 Iteration 50 Training Loss: 2.569e-01 Loss in Target Net: 4.221e-02

2020-02-02 10:49:54 Iteration 100 Training Loss: 2.346e-01 Loss in Target Net: 3.162e-02

2020-02-02 10:50:12 Iteration 150 Training Loss: 2.274e-01 Loss in Target Net: 3.487e-02

2020-02-02 10:50:30 Iteration 200 Training Loss: 2.169e-01 Loss in Target Net: 3.115e-02

2020-02-02 10:50:49 Iteration 250 Training Loss: 2.141e-01 Loss in Target Net: 3.251e-02

2020-02-02 10:51:07 Iteration 300 Training Loss: 2.073e-01 Loss in Target Net: 3.120e-02

2020-02-02 10:51:26 Iteration 350 Training Loss: 2.065e-01 Loss in Target Net: 3.040e-02

2020-02-02 10:51:44 Iteration 400 Training Loss: 2.051e-01 Loss in Target Net: 2.931e-02

2020-02-02 10:52:03 Iteration 450 Training Loss: 2.148e-01 Loss in Target Net: 3.760e-02

2020-02-02 10:52:22 Iteration 500 Training Loss: 2.039e-01 Loss in Target Net: 3.151e-02

2020-02-02 10:52:41 Iteration 550 Training Loss: 1.998e-01 Loss in Target Net: 3.428e-02

2020-02-02 10:53:00 Iteration 600 Training Loss: 2.014e-01 Loss in Target Net: 2.809e-02

2020-02-02 10:53:20 Iteration 650 Training Loss: 2.010e-01 Loss in Target Net: 3.119e-02

2020-02-02 10:53:39 Iteration 700 Training Loss: 2.025e-01 Loss in Target Net: 2.974e-02

2020-02-02 10:53:58 Iteration 750 Training Loss: 1.952e-01 Loss in Target Net: 2.999e-02

2020-02-02 10:54:18 Iteration 800 Training Loss: 1.988e-01 Loss in Target Net: 3.035e-02

2020-02-02 10:54:37 Iteration 850 Training Loss: 1.942e-01 Loss in Target Net: 3.124e-02

2020-02-02 10:54:56 Iteration 900 Training Loss: 1.988e-01 Loss in Target Net: 3.061e-02

2020-02-02 10:55:15 Iteration 950 Training Loss: 1.903e-01 Loss in Target Net: 2.537e-02

2020-02-02 10:55:34 Iteration 1000 Training Loss: 1.966e-01 Loss in Target Net: 2.304e-02

2020-02-02 10:55:53 Iteration 1050 Training Loss: 1.962e-01 Loss in Target Net: 2.530e-02

2020-02-02 10:56:12 Iteration 1100 Training Loss: 1.994e-01 Loss in Target Net: 2.777e-02

2020-02-02 10:56:30 Iteration 1150 Training Loss: 1.919e-01 Loss in Target Net: 2.935e-02

2020-02-02 10:56:48 Iteration 1200 Training Loss: 1.974e-01 Loss in Target Net: 2.385e-02

2020-02-02 10:57:08 Iteration 1250 Training Loss: 1.910e-01 Loss in Target Net: 2.582e-02

2020-02-02 10:57:27 Iteration 1300 Training Loss: 1.939e-01 Loss in Target Net: 2.335e-02

2020-02-02 10:57:47 Iteration 1350 Training Loss: 1.942e-01 Loss in Target Net: 2.602e-02

2020-02-02 10:58:09 Iteration 1400 Training Loss: 1.933e-01 Loss in Target Net: 3.487e-02

2020-02-02 10:58:29 Iteration 1450 Training Loss: 1.913e-01 Loss in Target Net: 2.352e-02

2020-02-02 10:58:49 Iteration 1499 Training Loss: 1.913e-01 Loss in Target Net: 3.005e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-02 10:58:58, Epoch 0, Iteration 7, loss 0.397 (0.413), acc 88.462 (91.400)

2020-02-02 10:59:57, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.3199525, -1.2360389, -3.0512056, 0.6286387, -1.0786347, -3.1990297, 6.2378592, -1.7196736, 5.9849772, -0.9120336], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-02 11:00:57 Epoch 59, Val iteration 0, acc 92.200 (92.200)

2020-02-02 11:01:04 Epoch 59, Val iteration 19, acc 91.200 (92.860)

* Prec: 92.86000175476075

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

TIME ELAPSED (mins): 9