text
stringlengths 5
1.13k
|
|---|
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-4.2527776, -0.71388316, -2.684365, 0.32620427, -1.5455924, 0.79475254, 7.3749743, 0.27104002, 3.8093677, -3.141781], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:56:05 Epoch 59, Val iteration 0, acc 93.600 (93.600)
|
2020-02-02 12:56:13 Epoch 59, Val iteration 19, acc 93.000 (93.300)
|
* Prec: 93.30000228881836
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 8
|
TARGET INDEX: 41
|
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=41, 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/1500/41
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-03 06:20:27 Iteration 0 Training Loss: 1.025e+00 Loss in Target Net: 1.403e+00
|
2020-02-03 06:20:43 Iteration 50 Training Loss: 2.666e-01 Loss in Target Net: 1.248e-01
|
2020-02-03 06:21:00 Iteration 100 Training Loss: 2.409e-01 Loss in Target Net: 8.165e-02
|
2020-02-03 06:21:16 Iteration 150 Training Loss: 2.286e-01 Loss in Target Net: 7.632e-02
|
2020-02-03 06:21:32 Iteration 200 Training Loss: 2.254e-01 Loss in Target Net: 6.483e-02
|
2020-02-03 06:21:51 Iteration 250 Training Loss: 2.196e-01 Loss in Target Net: 5.832e-02
|
2020-02-03 06:22:08 Iteration 300 Training Loss: 2.158e-01 Loss in Target Net: 5.177e-02
|
2020-02-03 06:22:26 Iteration 350 Training Loss: 2.167e-01 Loss in Target Net: 4.597e-02
|
2020-02-03 06:22:43 Iteration 400 Training Loss: 2.145e-01 Loss in Target Net: 5.040e-02
|
2020-02-03 06:22:59 Iteration 450 Training Loss: 2.072e-01 Loss in Target Net: 4.679e-02
|
2020-02-03 06:23:15 Iteration 500 Training Loss: 2.055e-01 Loss in Target Net: 4.558e-02
|
2020-02-03 06:23:32 Iteration 550 Training Loss: 2.093e-01 Loss in Target Net: 3.877e-02
|
2020-02-03 06:23:49 Iteration 600 Training Loss: 2.109e-01 Loss in Target Net: 4.694e-02
|
2020-02-03 06:24:06 Iteration 650 Training Loss: 2.069e-01 Loss in Target Net: 4.612e-02
|
2020-02-03 06:24:22 Iteration 700 Training Loss: 2.037e-01 Loss in Target Net: 4.508e-02
|
2020-02-03 06:24:40 Iteration 750 Training Loss: 2.033e-01 Loss in Target Net: 4.466e-02
|
2020-02-03 06:24:57 Iteration 800 Training Loss: 2.075e-01 Loss in Target Net: 4.366e-02
|
2020-02-03 06:25:15 Iteration 850 Training Loss: 2.038e-01 Loss in Target Net: 4.147e-02
|
2020-02-03 06:25:31 Iteration 900 Training Loss: 2.064e-01 Loss in Target Net: 4.979e-02
|
2020-02-03 06:25:48 Iteration 950 Training Loss: 2.080e-01 Loss in Target Net: 4.701e-02
|
2020-02-03 06:26:06 Iteration 1000 Training Loss: 2.038e-01 Loss in Target Net: 5.342e-02
|
2020-02-03 06:26:23 Iteration 1050 Training Loss: 1.995e-01 Loss in Target Net: 4.495e-02
|
2020-02-03 06:26:40 Iteration 1100 Training Loss: 2.008e-01 Loss in Target Net: 4.376e-02
|
2020-02-03 06:26:57 Iteration 1150 Training Loss: 2.077e-01 Loss in Target Net: 4.937e-02
|
2020-02-03 06:27:16 Iteration 1200 Training Loss: 2.021e-01 Loss in Target Net: 4.654e-02
|
2020-02-03 06:27:33 Iteration 1250 Training Loss: 2.021e-01 Loss in Target Net: 4.331e-02
|
2020-02-03 06:27:50 Iteration 1300 Training Loss: 2.021e-01 Loss in Target Net: 4.422e-02
|
2020-02-03 06:28:08 Iteration 1350 Training Loss: 1.991e-01 Loss in Target Net: 4.787e-02
|
2020-02-03 06:28:24 Iteration 1400 Training Loss: 2.018e-01 Loss in Target Net: 5.135e-02
|
2020-02-03 06:28:42 Iteration 1450 Training Loss: 1.991e-01 Loss in Target Net: 4.513e-02
|
2020-02-03 06:28:58 Iteration 1499 Training Loss: 2.006e-01 Loss in Target Net: 5.451e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-03 06:29:08, Epoch 0, Iteration 7, loss 0.347 (0.452), acc 90.385 (89.800)
|
2020-02-03 06:30:05, 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:[-4.0447197, -0.75518197, -3.4356034, -0.77645504, 0.8566292, 1.7794316, 5.6153035, -1.5742179, 5.4190764, -2.6574306], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-03 06:31:05 Epoch 59, Val iteration 0, acc 91.200 (91.200)
|
2020-02-03 06:31:13 Epoch 59, Val iteration 19, acc 92.400 (93.010)
|
* Prec: 93.01000213623047
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 8
|
TARGET INDEX: 41
|
DPN92 0
|
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='2', 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=42, 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/1500/42
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:44:16 Iteration 0 Training Loss: 1.034e+00 Loss in Target Net: 1.389e+00
|
2020-02-02 12:44:34 Iteration 50 Training Loss: 2.331e-01 Loss in Target Net: 3.426e-02
|
2020-02-02 12:44:51 Iteration 100 Training Loss: 2.090e-01 Loss in Target Net: 2.693e-02
|
2020-02-02 12:45:11 Iteration 150 Training Loss: 1.990e-01 Loss in Target Net: 2.622e-02
|
2020-02-02 12:45:30 Iteration 200 Training Loss: 1.946e-01 Loss in Target Net: 3.051e-02
|
2020-02-02 12:45:49 Iteration 250 Training Loss: 1.873e-01 Loss in Target Net: 2.978e-02
|
2020-02-02 12:46:08 Iteration 300 Training Loss: 1.865e-01 Loss in Target Net: 2.748e-02
|
2020-02-02 12:46:28 Iteration 350 Training Loss: 1.849e-01 Loss in Target Net: 2.827e-02
|
2020-02-02 12:46:46 Iteration 400 Training Loss: 1.847e-01 Loss in Target Net: 2.912e-02
|
2020-02-02 12:47:04 Iteration 450 Training Loss: 1.790e-01 Loss in Target Net: 3.019e-02
|
2020-02-02 12:47:22 Iteration 500 Training Loss: 1.832e-01 Loss in Target Net: 2.919e-02
|
2020-02-02 12:47:40 Iteration 550 Training Loss: 1.780e-01 Loss in Target Net: 2.600e-02
|
2020-02-02 12:47:58 Iteration 600 Training Loss: 1.777e-01 Loss in Target Net: 2.860e-02
|
2020-02-02 12:48:17 Iteration 650 Training Loss: 1.781e-01 Loss in Target Net: 2.734e-02
|
2020-02-02 12:48:38 Iteration 700 Training Loss: 1.777e-01 Loss in Target Net: 2.647e-02
|
2020-02-02 12:48:55 Iteration 750 Training Loss: 1.775e-01 Loss in Target Net: 2.733e-02
|
2020-02-02 12:49:14 Iteration 800 Training Loss: 1.744e-01 Loss in Target Net: 2.509e-02
|
2020-02-02 12:49:31 Iteration 850 Training Loss: 1.758e-01 Loss in Target Net: 2.664e-02
|
2020-02-02 12:49:49 Iteration 900 Training Loss: 1.748e-01 Loss in Target Net: 2.664e-02
|
2020-02-02 12:50:06 Iteration 950 Training Loss: 1.738e-01 Loss in Target Net: 2.482e-02
|
2020-02-02 12:50:23 Iteration 1000 Training Loss: 1.769e-01 Loss in Target Net: 2.583e-02
|
2020-02-02 12:50:42 Iteration 1050 Training Loss: 1.723e-01 Loss in Target Net: 2.015e-02
|
2020-02-02 12:51:01 Iteration 1100 Training Loss: 1.757e-01 Loss in Target Net: 2.187e-02
|
2020-02-02 12:51:18 Iteration 1150 Training Loss: 1.766e-01 Loss in Target Net: 2.526e-02
|
2020-02-02 12:51:37 Iteration 1200 Training Loss: 1.778e-01 Loss in Target Net: 2.392e-02
|
2020-02-02 12:51:55 Iteration 1250 Training Loss: 1.736e-01 Loss in Target Net: 2.229e-02
|
2020-02-02 12:52:13 Iteration 1300 Training Loss: 1.744e-01 Loss in Target Net: 2.160e-02
|
2020-02-02 12:52:31 Iteration 1350 Training Loss: 1.749e-01 Loss in Target Net: 2.893e-02
|
2020-02-02 12:52:50 Iteration 1400 Training Loss: 1.695e-01 Loss in Target Net: 2.218e-02
|
2020-02-02 12:53:08 Iteration 1450 Training Loss: 1.739e-01 Loss in Target Net: 2.194e-02
|
2020-02-02 12:53:26 Iteration 1499 Training Loss: 1.743e-01 Loss in Target Net: 2.146e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:53:36, Epoch 0, Iteration 7, loss 0.660 (0.413), acc 78.846 (90.800)
|
2020-02-02 12:54:33, 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:[-1.2413604, -2.0963652, -2.6873415, -1.7087985, -1.1076798, -3.8921735, 11.792459, -3.1855466, 4.9575195, -0.47003523], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:55:33 Epoch 59, Val iteration 0, acc 91.800 (91.800)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.