text
stringlengths 5
1.13k
|
|---|
2020-02-02 12:16:29 Iteration 1150 Training Loss: 1.696e-01 Loss in Target Net: 4.231e-02
|
2020-02-02 12:16:45 Iteration 1200 Training Loss: 1.715e-01 Loss in Target Net: 4.653e-02
|
2020-02-02 12:17:01 Iteration 1250 Training Loss: 1.725e-01 Loss in Target Net: 4.237e-02
|
2020-02-02 12:17:19 Iteration 1300 Training Loss: 1.672e-01 Loss in Target Net: 4.731e-02
|
2020-02-02 12:17:37 Iteration 1350 Training Loss: 1.704e-01 Loss in Target Net: 4.879e-02
|
2020-02-02 12:17:55 Iteration 1400 Training Loss: 1.749e-01 Loss in Target Net: 3.867e-02
|
2020-02-02 12:18:13 Iteration 1450 Training Loss: 1.705e-01 Loss in Target Net: 4.425e-02
|
2020-02-02 12:18:31 Iteration 1499 Training Loss: 1.661e-01 Loss in Target Net: 4.667e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:18:40, Epoch 0, Iteration 7, loss 0.207 (0.438), acc 92.308 (90.600)
|
2020-02-02 12:19:37, 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:[-4.5923567, -0.69015026, -1.3778999, 1.844934, -2.4719872, -0.4876977, 5.858692, -3.0685453, 8.405488, -2.914718], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:20:37 Epoch 59, Val iteration 0, acc 93.600 (93.600)
|
2020-02-02 12:20:44 Epoch 59, Val iteration 19, acc 92.800 (93.040)
|
* Prec: 93.04000091552734
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 8
|
TARGET INDEX: 30
|
DPN92 1
|
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=31, 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/1500/31
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:07:05 Iteration 0 Training Loss: 1.086e+00 Loss in Target Net: 1.473e+00
|
2020-02-02 12:07:23 Iteration 50 Training Loss: 2.560e-01 Loss in Target Net: 8.201e-02
|
2020-02-02 12:07:39 Iteration 100 Training Loss: 2.160e-01 Loss in Target Net: 7.567e-02
|
2020-02-02 12:07:55 Iteration 150 Training Loss: 2.014e-01 Loss in Target Net: 6.785e-02
|
2020-02-02 12:08:12 Iteration 200 Training Loss: 1.950e-01 Loss in Target Net: 4.464e-02
|
2020-02-02 12:08:30 Iteration 250 Training Loss: 1.878e-01 Loss in Target Net: 4.007e-02
|
2020-02-02 12:08:47 Iteration 300 Training Loss: 1.856e-01 Loss in Target Net: 4.266e-02
|
2020-02-02 12:09:06 Iteration 350 Training Loss: 1.810e-01 Loss in Target Net: 4.711e-02
|
2020-02-02 12:09:24 Iteration 400 Training Loss: 1.810e-01 Loss in Target Net: 4.134e-02
|
2020-02-02 12:09:41 Iteration 450 Training Loss: 1.823e-01 Loss in Target Net: 3.241e-02
|
2020-02-02 12:10:00 Iteration 500 Training Loss: 1.772e-01 Loss in Target Net: 3.307e-02
|
2020-02-02 12:10:17 Iteration 550 Training Loss: 1.761e-01 Loss in Target Net: 3.314e-02
|
2020-02-02 12:10:35 Iteration 600 Training Loss: 1.714e-01 Loss in Target Net: 3.076e-02
|
2020-02-02 12:10:53 Iteration 650 Training Loss: 1.743e-01 Loss in Target Net: 3.362e-02
|
2020-02-02 12:11:12 Iteration 700 Training Loss: 1.714e-01 Loss in Target Net: 3.265e-02
|
2020-02-02 12:11:29 Iteration 750 Training Loss: 1.723e-01 Loss in Target Net: 3.636e-02
|
2020-02-02 12:11:44 Iteration 800 Training Loss: 1.754e-01 Loss in Target Net: 3.184e-02
|
2020-02-02 12:12:00 Iteration 850 Training Loss: 1.682e-01 Loss in Target Net: 3.171e-02
|
2020-02-02 12:12:19 Iteration 900 Training Loss: 1.770e-01 Loss in Target Net: 3.314e-02
|
2020-02-02 12:12:38 Iteration 950 Training Loss: 1.748e-01 Loss in Target Net: 3.814e-02
|
2020-02-02 12:12:56 Iteration 1000 Training Loss: 1.715e-01 Loss in Target Net: 4.058e-02
|
2020-02-02 12:13:14 Iteration 1050 Training Loss: 1.662e-01 Loss in Target Net: 3.433e-02
|
2020-02-02 12:13:32 Iteration 1100 Training Loss: 1.659e-01 Loss in Target Net: 3.271e-02
|
2020-02-02 12:13:51 Iteration 1150 Training Loss: 1.708e-01 Loss in Target Net: 3.406e-02
|
2020-02-02 12:14:10 Iteration 1200 Training Loss: 1.689e-01 Loss in Target Net: 3.225e-02
|
2020-02-02 12:14:29 Iteration 1250 Training Loss: 1.665e-01 Loss in Target Net: 3.361e-02
|
2020-02-02 12:14:47 Iteration 1300 Training Loss: 1.655e-01 Loss in Target Net: 3.199e-02
|
2020-02-02 12:15:06 Iteration 1350 Training Loss: 1.739e-01 Loss in Target Net: 3.306e-02
|
2020-02-02 12:15:27 Iteration 1400 Training Loss: 1.666e-01 Loss in Target Net: 3.093e-02
|
2020-02-02 12:15:47 Iteration 1450 Training Loss: 1.726e-01 Loss in Target Net: 3.141e-02
|
2020-02-02 12:16:07 Iteration 1499 Training Loss: 1.648e-01 Loss in Target Net: 3.021e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:16:16, Epoch 0, Iteration 7, loss 0.612 (0.470), acc 80.769 (89.200)
|
2020-02-02 12:17:13, 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:[-2.2230182, -2.2627041, -0.7274695, -2.9461274, 2.1218116, -1.6670687, 4.814421, -2.4180815, 6.150696, -0.67169964], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:18:12 Epoch 59, Val iteration 0, acc 92.600 (92.600)
|
2020-02-02 12:18:19 Epoch 59, Val iteration 19, acc 93.400 (92.780)
|
* Prec: 92.78000144958496
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 9
|
TARGET INDEX: 31
|
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=32, 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/1500/32
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:21:19 Iteration 0 Training Loss: 1.018e+00 Loss in Target Net: 1.486e+00
|
2020-02-02 12:21:37 Iteration 50 Training Loss: 2.508e-01 Loss in Target Net: 7.962e-02
|
2020-02-02 12:21:55 Iteration 100 Training Loss: 2.331e-01 Loss in Target Net: 7.831e-02
|
2020-02-02 12:22:12 Iteration 150 Training Loss: 2.127e-01 Loss in Target Net: 7.714e-02
|
2020-02-02 12:22:29 Iteration 200 Training Loss: 2.074e-01 Loss in Target Net: 9.619e-02
|
2020-02-02 12:22:46 Iteration 250 Training Loss: 1.977e-01 Loss in Target Net: 7.712e-02
|
2020-02-02 12:23:02 Iteration 300 Training Loss: 2.029e-01 Loss in Target Net: 6.010e-02
|
2020-02-02 12:23:20 Iteration 350 Training Loss: 1.959e-01 Loss in Target Net: 6.146e-02
|
2020-02-02 12:23:37 Iteration 400 Training Loss: 2.011e-01 Loss in Target Net: 7.252e-02
|
2020-02-02 12:23:54 Iteration 450 Training Loss: 1.948e-01 Loss in Target Net: 6.330e-02
|
2020-02-02 12:24:11 Iteration 500 Training Loss: 1.975e-01 Loss in Target Net: 6.497e-02
|
2020-02-02 12:24:29 Iteration 550 Training Loss: 1.972e-01 Loss in Target Net: 6.007e-02
|
2020-02-02 12:24:45 Iteration 600 Training Loss: 1.900e-01 Loss in Target Net: 7.326e-02
|
2020-02-02 12:25:03 Iteration 650 Training Loss: 1.974e-01 Loss in Target Net: 5.121e-02
|
2020-02-02 12:25:19 Iteration 700 Training Loss: 1.895e-01 Loss in Target Net: 5.358e-02
|
2020-02-02 12:25:36 Iteration 750 Training Loss: 1.919e-01 Loss in Target Net: 5.341e-02
|
2020-02-02 12:25:52 Iteration 800 Training Loss: 1.890e-01 Loss in Target Net: 5.898e-02
|
2020-02-02 12:26:09 Iteration 850 Training Loss: 1.890e-01 Loss in Target Net: 8.128e-02
|
2020-02-02 12:26:25 Iteration 900 Training Loss: 1.892e-01 Loss in Target Net: 6.793e-02
|
2020-02-02 12:26:42 Iteration 950 Training Loss: 1.889e-01 Loss in Target Net: 6.131e-02
|
2020-02-02 12:26:59 Iteration 1000 Training Loss: 1.869e-01 Loss in Target Net: 7.470e-02
|
2020-02-02 12:27:16 Iteration 1050 Training Loss: 1.895e-01 Loss in Target Net: 6.070e-02
|
2020-02-02 12:27:34 Iteration 1100 Training Loss: 1.921e-01 Loss in Target Net: 5.558e-02
|
2020-02-02 12:27:51 Iteration 1150 Training Loss: 1.945e-01 Loss in Target Net: 5.923e-02
|
2020-02-02 12:28:08 Iteration 1200 Training Loss: 1.900e-01 Loss in Target Net: 7.428e-02
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.