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='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=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=11, 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/1500/11
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 11:11:56 Iteration 0 Training Loss: 1.046e+00 Loss in Target Net: 1.611e+00
2020-02-02 11:12:13 Iteration 50 Training Loss: 2.919e-01 Loss in Target Net: 1.216e-01
2020-02-02 11:12:31 Iteration 100 Training Loss: 2.528e-01 Loss in Target Net: 7.462e-02
2020-02-02 11:12:48 Iteration 150 Training Loss: 2.375e-01 Loss in Target Net: 6.432e-02
2020-02-02 11:13:06 Iteration 200 Training Loss: 2.310e-01 Loss in Target Net: 6.038e-02
2020-02-02 11:13:24 Iteration 250 Training Loss: 2.292e-01 Loss in Target Net: 4.957e-02
2020-02-02 11:13:42 Iteration 300 Training Loss: 2.214e-01 Loss in Target Net: 5.245e-02
2020-02-02 11:14:00 Iteration 350 Training Loss: 2.159e-01 Loss in Target Net: 4.399e-02
2020-02-02 11:14:17 Iteration 400 Training Loss: 2.209e-01 Loss in Target Net: 4.609e-02
2020-02-02 11:14:35 Iteration 450 Training Loss: 2.129e-01 Loss in Target Net: 4.416e-02
2020-02-02 11:14:52 Iteration 500 Training Loss: 2.105e-01 Loss in Target Net: 4.889e-02
2020-02-02 11:15:10 Iteration 550 Training Loss: 2.060e-01 Loss in Target Net: 3.860e-02
2020-02-02 11:15:27 Iteration 600 Training Loss: 2.073e-01 Loss in Target Net: 5.146e-02
2020-02-02 11:15:45 Iteration 650 Training Loss: 2.058e-01 Loss in Target Net: 4.328e-02
2020-02-02 11:16:02 Iteration 700 Training Loss: 2.076e-01 Loss in Target Net: 5.050e-02
2020-02-02 11:16:20 Iteration 750 Training Loss: 2.033e-01 Loss in Target Net: 3.747e-02
2020-02-02 11:16:37 Iteration 800 Training Loss: 2.068e-01 Loss in Target Net: 3.789e-02
2020-02-02 11:16:55 Iteration 850 Training Loss: 2.039e-01 Loss in Target Net: 3.927e-02
2020-02-02 11:17:12 Iteration 900 Training Loss: 2.040e-01 Loss in Target Net: 3.737e-02
2020-02-02 11:17:30 Iteration 950 Training Loss: 2.117e-01 Loss in Target Net: 4.089e-02
2020-02-02 11:17:47 Iteration 1000 Training Loss: 1.991e-01 Loss in Target Net: 4.417e-02
2020-02-02 11:18:04 Iteration 1050 Training Loss: 1.971e-01 Loss in Target Net: 4.225e-02
2020-02-02 11:18:21 Iteration 1100 Training Loss: 2.021e-01 Loss in Target Net: 3.835e-02
2020-02-02 11:18:39 Iteration 1150 Training Loss: 2.038e-01 Loss in Target Net: 3.507e-02
2020-02-02 11:18:57 Iteration 1200 Training Loss: 2.016e-01 Loss in Target Net: 3.148e-02
2020-02-02 11:19:15 Iteration 1250 Training Loss: 2.014e-01 Loss in Target Net: 3.913e-02
2020-02-02 11:19:32 Iteration 1300 Training Loss: 1.943e-01 Loss in Target Net: 3.966e-02
2020-02-02 11:19:49 Iteration 1350 Training Loss: 2.109e-01 Loss in Target Net: 3.883e-02
2020-02-02 11:20:06 Iteration 1400 Training Loss: 2.023e-01 Loss in Target Net: 3.418e-02
2020-02-02 11:20:25 Iteration 1450 Training Loss: 1.955e-01 Loss in Target Net: 3.716e-02
2020-02-02 11:20:43 Iteration 1499 Training Loss: 2.018e-01 Loss in Target Net: 4.125e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-02 11:20:53, Epoch 0, Iteration 7, loss 0.432 (0.498), acc 88.462 (89.600)
2020-02-02 11:21:50, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-1.0916842, -0.06402114, -1.4954817, -2.8581793, -2.3661954, -1.0123562, 4.4887295, -0.44003546, 6.014351, -0.923673], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-02 11:22:50 Epoch 59, Val iteration 0, acc 91.400 (91.400)
2020-02-02 11:22:58 Epoch 59, Val iteration 19, acc 92.600 (92.700)
* Prec: 92.7000015258789
--------
------SUMMARY------
TIME ELAPSED (mins): 8
TARGET INDEX: 11
DPN92 1
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=12, 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/1500/12
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 11:23:19 Iteration 0 Training Loss: 9.811e-01 Loss in Target Net: 1.404e+00
2020-02-02 11:23:35 Iteration 50 Training Loss: 2.856e-01 Loss in Target Net: 1.368e-01
2020-02-02 11:23:53 Iteration 100 Training Loss: 2.486e-01 Loss in Target Net: 1.068e-01
2020-02-02 11:24:10 Iteration 150 Training Loss: 2.365e-01 Loss in Target Net: 1.252e-01
2020-02-02 11:24:28 Iteration 200 Training Loss: 2.275e-01 Loss in Target Net: 1.089e-01
2020-02-02 11:24:44 Iteration 250 Training Loss: 2.225e-01 Loss in Target Net: 9.860e-02
2020-02-02 11:25:03 Iteration 300 Training Loss: 2.180e-01 Loss in Target Net: 8.976e-02
2020-02-02 11:25:19 Iteration 350 Training Loss: 2.127e-01 Loss in Target Net: 7.268e-02
2020-02-02 11:25:37 Iteration 400 Training Loss: 2.123e-01 Loss in Target Net: 6.134e-02
2020-02-02 11:25:56 Iteration 450 Training Loss: 2.116e-01 Loss in Target Net: 6.562e-02
2020-02-02 11:26:14 Iteration 500 Training Loss: 2.124e-01 Loss in Target Net: 7.934e-02
2020-02-02 11:26:31 Iteration 550 Training Loss: 2.078e-01 Loss in Target Net: 8.966e-02
2020-02-02 11:26:49 Iteration 600 Training Loss: 2.098e-01 Loss in Target Net: 8.841e-02
2020-02-02 11:27:06 Iteration 650 Training Loss: 2.117e-01 Loss in Target Net: 7.813e-02
2020-02-02 11:27:23 Iteration 700 Training Loss: 2.171e-01 Loss in Target Net: 8.589e-02
2020-02-02 11:27:40 Iteration 750 Training Loss: 2.066e-01 Loss in Target Net: 9.100e-02
2020-02-02 11:27:57 Iteration 800 Training Loss: 2.091e-01 Loss in Target Net: 7.845e-02
2020-02-02 11:28:14 Iteration 850 Training Loss: 2.113e-01 Loss in Target Net: 1.011e-01
2020-02-02 11:28:32 Iteration 900 Training Loss: 2.040e-01 Loss in Target Net: 9.345e-02
2020-02-02 11:28:48 Iteration 950 Training Loss: 2.136e-01 Loss in Target Net: 9.867e-02
2020-02-02 11:29:06 Iteration 1000 Training Loss: 1.991e-01 Loss in Target Net: 7.952e-02
2020-02-02 11:29:25 Iteration 1050 Training Loss: 2.050e-01 Loss in Target Net: 6.653e-02
2020-02-02 11:29:43 Iteration 1100 Training Loss: 2.038e-01 Loss in Target Net: 6.844e-02
2020-02-02 11:30:02 Iteration 1150 Training Loss: 2.057e-01 Loss in Target Net: 1.013e-01
2020-02-02 11:30:21 Iteration 1200 Training Loss: 2.064e-01 Loss in Target Net: 1.060e-01
2020-02-02 11:30:38 Iteration 1250 Training Loss: 1.975e-01 Loss in Target Net: 6.588e-02
2020-02-02 11:30:59 Iteration 1300 Training Loss: 2.088e-01 Loss in Target Net: 8.330e-02
2020-02-02 11:31:18 Iteration 1350 Training Loss: 2.093e-01 Loss in Target Net: 7.317e-02
2020-02-02 11:31:37 Iteration 1400 Training Loss: 2.084e-01 Loss in Target Net: 7.724e-02
2020-02-02 11:31:55 Iteration 1450 Training Loss: 1.998e-01 Loss in Target Net: 9.692e-02
2020-02-02 11:32:13 Iteration 1499 Training Loss: 1.996e-01 Loss in Target Net: 1.058e-01
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-02 11:32:22, Epoch 0, Iteration 7, loss 0.428 (0.476), acc 86.538 (89.400)
2020-02-02 11:33:20, 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:[-2.953826, -1.8295789, -0.87282896, -2.6328607, -0.95264685, 2.3238902, 8.800332, -3.9041264, 5.2996197, -2.8040237], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-02 11:34:20 Epoch 59, Val iteration 0, acc 93.000 (93.000)
2020-02-02 11:34:27 Epoch 59, Val iteration 19, acc 94.000 (93.740)
* Prec: 93.7400016784668
--------
------SUMMARY------
TIME ELAPSED (mins): 8
TARGET INDEX: 12
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=13, 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/1500/13

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=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=11, 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/1500/11

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

2020-02-02 11:11:56 Iteration 0 Training Loss: 1.046e+00 Loss in Target Net: 1.611e+00

2020-02-02 11:12:13 Iteration 50 Training Loss: 2.919e-01 Loss in Target Net: 1.216e-01

2020-02-02 11:12:31 Iteration 100 Training Loss: 2.528e-01 Loss in Target Net: 7.462e-02

2020-02-02 11:12:48 Iteration 150 Training Loss: 2.375e-01 Loss in Target Net: 6.432e-02

2020-02-02 11:13:06 Iteration 200 Training Loss: 2.310e-01 Loss in Target Net: 6.038e-02

2020-02-02 11:13:24 Iteration 250 Training Loss: 2.292e-01 Loss in Target Net: 4.957e-02

2020-02-02 11:13:42 Iteration 300 Training Loss: 2.214e-01 Loss in Target Net: 5.245e-02

2020-02-02 11:14:00 Iteration 350 Training Loss: 2.159e-01 Loss in Target Net: 4.399e-02

2020-02-02 11:14:17 Iteration 400 Training Loss: 2.209e-01 Loss in Target Net: 4.609e-02

2020-02-02 11:14:35 Iteration 450 Training Loss: 2.129e-01 Loss in Target Net: 4.416e-02

2020-02-02 11:14:52 Iteration 500 Training Loss: 2.105e-01 Loss in Target Net: 4.889e-02

2020-02-02 11:15:10 Iteration 550 Training Loss: 2.060e-01 Loss in Target Net: 3.860e-02

2020-02-02 11:15:27 Iteration 600 Training Loss: 2.073e-01 Loss in Target Net: 5.146e-02

2020-02-02 11:15:45 Iteration 650 Training Loss: 2.058e-01 Loss in Target Net: 4.328e-02

2020-02-02 11:16:02 Iteration 700 Training Loss: 2.076e-01 Loss in Target Net: 5.050e-02

2020-02-02 11:16:20 Iteration 750 Training Loss: 2.033e-01 Loss in Target Net: 3.747e-02

2020-02-02 11:16:37 Iteration 800 Training Loss: 2.068e-01 Loss in Target Net: 3.789e-02

2020-02-02 11:16:55 Iteration 850 Training Loss: 2.039e-01 Loss in Target Net: 3.927e-02

2020-02-02 11:17:12 Iteration 900 Training Loss: 2.040e-01 Loss in Target Net: 3.737e-02

2020-02-02 11:17:30 Iteration 950 Training Loss: 2.117e-01 Loss in Target Net: 4.089e-02

2020-02-02 11:17:47 Iteration 1000 Training Loss: 1.991e-01 Loss in Target Net: 4.417e-02

2020-02-02 11:18:04 Iteration 1050 Training Loss: 1.971e-01 Loss in Target Net: 4.225e-02

2020-02-02 11:18:21 Iteration 1100 Training Loss: 2.021e-01 Loss in Target Net: 3.835e-02

2020-02-02 11:18:39 Iteration 1150 Training Loss: 2.038e-01 Loss in Target Net: 3.507e-02

2020-02-02 11:18:57 Iteration 1200 Training Loss: 2.016e-01 Loss in Target Net: 3.148e-02

2020-02-02 11:19:15 Iteration 1250 Training Loss: 2.014e-01 Loss in Target Net: 3.913e-02

2020-02-02 11:19:32 Iteration 1300 Training Loss: 1.943e-01 Loss in Target Net: 3.966e-02

2020-02-02 11:19:49 Iteration 1350 Training Loss: 2.109e-01 Loss in Target Net: 3.883e-02

2020-02-02 11:20:06 Iteration 1400 Training Loss: 2.023e-01 Loss in Target Net: 3.418e-02

2020-02-02 11:20:25 Iteration 1450 Training Loss: 1.955e-01 Loss in Target Net: 3.716e-02

2020-02-02 11:20:43 Iteration 1499 Training Loss: 2.018e-01 Loss in Target Net: 4.125e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-02 11:20:53, Epoch 0, Iteration 7, loss 0.432 (0.498), acc 88.462 (89.600)

2020-02-02 11:21:50, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-1.0916842, -0.06402114, -1.4954817, -2.8581793, -2.3661954, -1.0123562, 4.4887295, -0.44003546, 6.014351, -0.923673], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-02 11:22:50 Epoch 59, Val iteration 0, acc 91.400 (91.400)

2020-02-02 11:22:58 Epoch 59, Val iteration 19, acc 92.600 (92.700)

* Prec: 92.7000015258789

--------

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

TIME ELAPSED (mins): 8

TARGET INDEX: 11

DPN92 1

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=12, 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/1500/12

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

2020-02-02 11:23:19 Iteration 0 Training Loss: 9.811e-01 Loss in Target Net: 1.404e+00

2020-02-02 11:23:35 Iteration 50 Training Loss: 2.856e-01 Loss in Target Net: 1.368e-01

2020-02-02 11:23:53 Iteration 100 Training Loss: 2.486e-01 Loss in Target Net: 1.068e-01

2020-02-02 11:24:10 Iteration 150 Training Loss: 2.365e-01 Loss in Target Net: 1.252e-01

2020-02-02 11:24:28 Iteration 200 Training Loss: 2.275e-01 Loss in Target Net: 1.089e-01

2020-02-02 11:24:44 Iteration 250 Training Loss: 2.225e-01 Loss in Target Net: 9.860e-02

2020-02-02 11:25:03 Iteration 300 Training Loss: 2.180e-01 Loss in Target Net: 8.976e-02

2020-02-02 11:25:19 Iteration 350 Training Loss: 2.127e-01 Loss in Target Net: 7.268e-02

2020-02-02 11:25:37 Iteration 400 Training Loss: 2.123e-01 Loss in Target Net: 6.134e-02

2020-02-02 11:25:56 Iteration 450 Training Loss: 2.116e-01 Loss in Target Net: 6.562e-02

2020-02-02 11:26:14 Iteration 500 Training Loss: 2.124e-01 Loss in Target Net: 7.934e-02

2020-02-02 11:26:31 Iteration 550 Training Loss: 2.078e-01 Loss in Target Net: 8.966e-02

2020-02-02 11:26:49 Iteration 600 Training Loss: 2.098e-01 Loss in Target Net: 8.841e-02

2020-02-02 11:27:06 Iteration 650 Training Loss: 2.117e-01 Loss in Target Net: 7.813e-02

2020-02-02 11:27:23 Iteration 700 Training Loss: 2.171e-01 Loss in Target Net: 8.589e-02

2020-02-02 11:27:40 Iteration 750 Training Loss: 2.066e-01 Loss in Target Net: 9.100e-02

2020-02-02 11:27:57 Iteration 800 Training Loss: 2.091e-01 Loss in Target Net: 7.845e-02

2020-02-02 11:28:14 Iteration 850 Training Loss: 2.113e-01 Loss in Target Net: 1.011e-01

2020-02-02 11:28:32 Iteration 900 Training Loss: 2.040e-01 Loss in Target Net: 9.345e-02

2020-02-02 11:28:48 Iteration 950 Training Loss: 2.136e-01 Loss in Target Net: 9.867e-02

2020-02-02 11:29:06 Iteration 1000 Training Loss: 1.991e-01 Loss in Target Net: 7.952e-02

2020-02-02 11:29:25 Iteration 1050 Training Loss: 2.050e-01 Loss in Target Net: 6.653e-02

2020-02-02 11:29:43 Iteration 1100 Training Loss: 2.038e-01 Loss in Target Net: 6.844e-02

2020-02-02 11:30:02 Iteration 1150 Training Loss: 2.057e-01 Loss in Target Net: 1.013e-01

2020-02-02 11:30:21 Iteration 1200 Training Loss: 2.064e-01 Loss in Target Net: 1.060e-01

2020-02-02 11:30:38 Iteration 1250 Training Loss: 1.975e-01 Loss in Target Net: 6.588e-02

2020-02-02 11:30:59 Iteration 1300 Training Loss: 2.088e-01 Loss in Target Net: 8.330e-02

2020-02-02 11:31:18 Iteration 1350 Training Loss: 2.093e-01 Loss in Target Net: 7.317e-02

2020-02-02 11:31:37 Iteration 1400 Training Loss: 2.084e-01 Loss in Target Net: 7.724e-02

2020-02-02 11:31:55 Iteration 1450 Training Loss: 1.998e-01 Loss in Target Net: 9.692e-02

2020-02-02 11:32:13 Iteration 1499 Training Loss: 1.996e-01 Loss in Target Net: 1.058e-01

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-02 11:32:22, Epoch 0, Iteration 7, loss 0.428 (0.476), acc 86.538 (89.400)

2020-02-02 11:33:20, 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:[-2.953826, -1.8295789, -0.87282896, -2.6328607, -0.95264685, 2.3238902, 8.800332, -3.9041264, 5.2996197, -2.8040237], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-02 11:34:20 Epoch 59, Val iteration 0, acc 93.000 (93.000)

2020-02-02 11:34:27 Epoch 59, Val iteration 19, acc 94.000 (93.740)

* Prec: 93.7400016784668

--------

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

TIME ELAPSED (mins): 8

TARGET INDEX: 12

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=13, 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/1500/13