text
stringlengths 5
1.13k
|
|---|
2020-02-02 12:15:25 Iteration 950 Training Loss: 1.634e-01 Loss in Target Net: 2.460e-02
|
2020-02-02 12:15:44 Iteration 1000 Training Loss: 1.618e-01 Loss in Target Net: 1.767e-02
|
2020-02-02 12:16:03 Iteration 1050 Training Loss: 1.655e-01 Loss in Target Net: 2.188e-02
|
2020-02-02 12:16:22 Iteration 1100 Training Loss: 1.577e-01 Loss in Target Net: 2.123e-02
|
2020-02-02 12:16:41 Iteration 1150 Training Loss: 1.615e-01 Loss in Target Net: 2.006e-02
|
2020-02-02 12:16:59 Iteration 1200 Training Loss: 1.589e-01 Loss in Target Net: 1.972e-02
|
2020-02-02 12:17:17 Iteration 1250 Training Loss: 1.571e-01 Loss in Target Net: 2.100e-02
|
2020-02-02 12:17:34 Iteration 1300 Training Loss: 1.605e-01 Loss in Target Net: 2.131e-02
|
2020-02-02 12:17:54 Iteration 1350 Training Loss: 1.598e-01 Loss in Target Net: 2.208e-02
|
2020-02-02 12:18:14 Iteration 1400 Training Loss: 1.619e-01 Loss in Target Net: 2.126e-02
|
2020-02-02 12:18:32 Iteration 1450 Training Loss: 1.642e-01 Loss in Target Net: 2.214e-02
|
2020-02-02 12:18:50 Iteration 1499 Training Loss: 1.635e-01 Loss in Target Net: 2.143e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:19:00, Epoch 0, Iteration 7, loss 0.188 (0.489), acc 92.308 (88.400)
|
2020-02-02 12:19:57, 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:[-2.2953024, -0.5815128, -0.43455455, -1.1994807, -1.3738041, -4.9408937, 4.191276, -3.2774267, 11.418906, -1.1022406], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:20:59 Epoch 59, Val iteration 0, acc 92.800 (92.800)
|
2020-02-02 12:21:06 Epoch 59, Val iteration 19, acc 92.800 (93.070)
|
* Prec: 93.0700023651123
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 9
|
TARGET INDEX: 28
|
DPN92 1
|
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=29, 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/1500/29
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:11:03 Iteration 0 Training Loss: 9.905e-01 Loss in Target Net: 1.341e+00
|
2020-02-02 12:11:21 Iteration 50 Training Loss: 2.729e-01 Loss in Target Net: 7.445e-02
|
2020-02-02 12:11:38 Iteration 100 Training Loss: 2.452e-01 Loss in Target Net: 7.078e-02
|
2020-02-02 12:11:57 Iteration 150 Training Loss: 2.332e-01 Loss in Target Net: 5.247e-02
|
2020-02-02 12:12:14 Iteration 200 Training Loss: 2.269e-01 Loss in Target Net: 5.459e-02
|
2020-02-02 12:12:31 Iteration 250 Training Loss: 2.154e-01 Loss in Target Net: 5.263e-02
|
2020-02-02 12:12:49 Iteration 300 Training Loss: 2.176e-01 Loss in Target Net: 4.890e-02
|
2020-02-02 12:13:06 Iteration 350 Training Loss: 2.102e-01 Loss in Target Net: 4.892e-02
|
2020-02-02 12:13:23 Iteration 400 Training Loss: 2.107e-01 Loss in Target Net: 5.403e-02
|
2020-02-02 12:13:42 Iteration 450 Training Loss: 2.083e-01 Loss in Target Net: 4.549e-02
|
2020-02-02 12:13:59 Iteration 500 Training Loss: 2.130e-01 Loss in Target Net: 4.630e-02
|
2020-02-02 12:14:16 Iteration 550 Training Loss: 2.061e-01 Loss in Target Net: 5.389e-02
|
2020-02-02 12:14:32 Iteration 600 Training Loss: 2.058e-01 Loss in Target Net: 5.119e-02
|
2020-02-02 12:14:49 Iteration 650 Training Loss: 2.024e-01 Loss in Target Net: 5.400e-02
|
2020-02-02 12:15:06 Iteration 700 Training Loss: 2.024e-01 Loss in Target Net: 5.166e-02
|
2020-02-02 12:15:23 Iteration 750 Training Loss: 2.040e-01 Loss in Target Net: 5.976e-02
|
2020-02-02 12:15:40 Iteration 800 Training Loss: 2.035e-01 Loss in Target Net: 4.953e-02
|
2020-02-02 12:15:57 Iteration 850 Training Loss: 2.009e-01 Loss in Target Net: 4.828e-02
|
2020-02-02 12:16:14 Iteration 900 Training Loss: 2.012e-01 Loss in Target Net: 4.453e-02
|
2020-02-02 12:16:31 Iteration 950 Training Loss: 2.007e-01 Loss in Target Net: 5.302e-02
|
2020-02-02 12:16:47 Iteration 1000 Training Loss: 2.074e-01 Loss in Target Net: 5.216e-02
|
2020-02-02 12:17:04 Iteration 1050 Training Loss: 1.975e-01 Loss in Target Net: 4.767e-02
|
2020-02-02 12:17:21 Iteration 1100 Training Loss: 2.044e-01 Loss in Target Net: 5.127e-02
|
2020-02-02 12:17:37 Iteration 1150 Training Loss: 2.033e-01 Loss in Target Net: 4.997e-02
|
2020-02-02 12:17:55 Iteration 1200 Training Loss: 1.976e-01 Loss in Target Net: 6.447e-02
|
2020-02-02 12:18:12 Iteration 1250 Training Loss: 2.027e-01 Loss in Target Net: 5.602e-02
|
2020-02-02 12:18:29 Iteration 1300 Training Loss: 2.014e-01 Loss in Target Net: 5.409e-02
|
2020-02-02 12:18:46 Iteration 1350 Training Loss: 1.986e-01 Loss in Target Net: 5.980e-02
|
2020-02-02 12:19:03 Iteration 1400 Training Loss: 1.974e-01 Loss in Target Net: 5.419e-02
|
2020-02-02 12:19:23 Iteration 1450 Training Loss: 1.999e-01 Loss in Target Net: 5.435e-02
|
2020-02-02 12:19:39 Iteration 1499 Training Loss: 1.961e-01 Loss in Target Net: 5.847e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:19:49, Epoch 0, Iteration 7, loss 0.887 (0.522), acc 80.769 (91.000)
|
2020-02-02 12:20:47, 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:[-3.41321, 0.08207241, 0.8376048, -0.5576196, -3.4269328, -3.2434754, 7.837949, -1.3506185, 7.6175585, -3.5705767], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:21:47 Epoch 59, Val iteration 0, acc 95.400 (95.400)
|
2020-02-02 12:21:54 Epoch 59, Val iteration 19, acc 92.400 (92.980)
|
* Prec: 92.98000144958496
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 8
|
TARGET INDEX: 29
|
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=29, 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/1500/29
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-03 05:34:20 Iteration 0 Training Loss: 1.003e+00 Loss in Target Net: 1.341e+00
|
2020-02-03 05:34:52 Iteration 50 Training Loss: 2.786e-01 Loss in Target Net: 7.264e-02
|
2020-02-03 05:35:21 Iteration 100 Training Loss: 2.519e-01 Loss in Target Net: 5.994e-02
|
2020-02-03 05:35:58 Iteration 150 Training Loss: 2.402e-01 Loss in Target Net: 5.871e-02
|
2020-02-03 05:36:41 Iteration 200 Training Loss: 2.389e-01 Loss in Target Net: 5.600e-02
|
2020-02-03 05:37:11 Iteration 250 Training Loss: 2.303e-01 Loss in Target Net: 5.834e-02
|
2020-02-03 05:37:43 Iteration 300 Training Loss: 2.234e-01 Loss in Target Net: 6.488e-02
|
2020-02-03 05:38:18 Iteration 350 Training Loss: 2.226e-01 Loss in Target Net: 5.656e-02
|
2020-02-03 05:38:56 Iteration 400 Training Loss: 2.227e-01 Loss in Target Net: 5.989e-02
|
2020-02-03 05:39:25 Iteration 450 Training Loss: 2.189e-01 Loss in Target Net: 5.750e-02
|
2020-02-03 05:40:02 Iteration 500 Training Loss: 2.133e-01 Loss in Target Net: 5.899e-02
|
2020-02-03 05:40:34 Iteration 550 Training Loss: 2.119e-01 Loss in Target Net: 7.405e-02
|
2020-02-03 05:41:04 Iteration 600 Training Loss: 2.074e-01 Loss in Target Net: 6.206e-02
|
2020-02-03 05:41:35 Iteration 650 Training Loss: 2.140e-01 Loss in Target Net: 5.420e-02
|
2020-02-03 05:42:09 Iteration 700 Training Loss: 2.092e-01 Loss in Target Net: 7.174e-02
|
2020-02-03 05:42:44 Iteration 750 Training Loss: 2.126e-01 Loss in Target Net: 5.608e-02
|
2020-02-03 05:43:18 Iteration 800 Training Loss: 2.099e-01 Loss in Target Net: 6.437e-02
|
2020-02-03 05:43:51 Iteration 850 Training Loss: 2.128e-01 Loss in Target Net: 6.199e-02
|
2020-02-03 05:44:24 Iteration 900 Training Loss: 2.134e-01 Loss in Target Net: 6.742e-02
|
2020-02-03 05:44:58 Iteration 950 Training Loss: 2.132e-01 Loss in Target Net: 7.477e-02
|
2020-02-03 05:45:26 Iteration 1000 Training Loss: 2.061e-01 Loss in Target Net: 8.261e-02
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.