text
stringlengths 5
1.13k
|
|---|
2020-02-03 06:05:31 Iteration 1350 Training Loss: 1.803e-01 Loss in Target Net: 3.491e-02
|
2020-02-03 06:05:48 Iteration 1400 Training Loss: 1.846e-01 Loss in Target Net: 4.155e-02
|
2020-02-03 06:06:05 Iteration 1450 Training Loss: 1.790e-01 Loss in Target Net: 3.754e-02
|
2020-02-03 06:06:24 Iteration 1499 Training Loss: 1.793e-01 Loss in Target Net: 3.584e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-03 06:06:33, Epoch 0, Iteration 7, loss 0.393 (0.528), acc 90.385 (89.200)
|
2020-02-03 06:07:30, 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:[-2.8485036, 0.36521998, -1.4030491, -0.4967751, -2.8856537, -2.7284057, 12.874115, -4.0331354, 5.3947296, -3.6846085], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-03 06:08:30 Epoch 59, Val iteration 0, acc 93.400 (93.400)
|
2020-02-03 06:08:37 Epoch 59, Val iteration 19, acc 93.800 (93.010)
|
* Prec: 93.01000137329102
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 11
|
TARGET INDEX: 33
|
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=34, 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/1500/34
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:20:57 Iteration 0 Training Loss: 1.051e+00 Loss in Target Net: 1.376e+00
|
2020-02-02 12:21:14 Iteration 50 Training Loss: 2.537e-01 Loss in Target Net: 1.057e-01
|
2020-02-02 12:21:32 Iteration 100 Training Loss: 2.271e-01 Loss in Target Net: 6.648e-02
|
2020-02-02 12:21:50 Iteration 150 Training Loss: 2.171e-01 Loss in Target Net: 5.137e-02
|
2020-02-02 12:22:08 Iteration 200 Training Loss: 2.079e-01 Loss in Target Net: 4.840e-02
|
2020-02-02 12:22:24 Iteration 250 Training Loss: 2.074e-01 Loss in Target Net: 5.232e-02
|
2020-02-02 12:22:43 Iteration 300 Training Loss: 2.046e-01 Loss in Target Net: 5.036e-02
|
2020-02-02 12:23:01 Iteration 350 Training Loss: 1.976e-01 Loss in Target Net: 4.676e-02
|
2020-02-02 12:23:22 Iteration 400 Training Loss: 1.952e-01 Loss in Target Net: 4.540e-02
|
2020-02-02 12:23:40 Iteration 450 Training Loss: 1.944e-01 Loss in Target Net: 4.678e-02
|
2020-02-02 12:23:57 Iteration 500 Training Loss: 1.935e-01 Loss in Target Net: 4.484e-02
|
2020-02-02 12:24:15 Iteration 550 Training Loss: 1.935e-01 Loss in Target Net: 4.979e-02
|
2020-02-02 12:24:32 Iteration 600 Training Loss: 1.922e-01 Loss in Target Net: 4.434e-02
|
2020-02-02 12:24:50 Iteration 650 Training Loss: 1.940e-01 Loss in Target Net: 3.885e-02
|
2020-02-02 12:25:09 Iteration 700 Training Loss: 1.885e-01 Loss in Target Net: 4.926e-02
|
2020-02-02 12:25:29 Iteration 750 Training Loss: 1.900e-01 Loss in Target Net: 3.705e-02
|
2020-02-02 12:25:47 Iteration 800 Training Loss: 1.860e-01 Loss in Target Net: 3.937e-02
|
2020-02-02 12:26:03 Iteration 850 Training Loss: 1.906e-01 Loss in Target Net: 4.497e-02
|
2020-02-02 12:26:22 Iteration 900 Training Loss: 1.826e-01 Loss in Target Net: 3.890e-02
|
2020-02-02 12:26:41 Iteration 950 Training Loss: 1.870e-01 Loss in Target Net: 3.662e-02
|
2020-02-02 12:26:59 Iteration 1000 Training Loss: 1.863e-01 Loss in Target Net: 3.441e-02
|
2020-02-02 12:27:17 Iteration 1050 Training Loss: 1.869e-01 Loss in Target Net: 3.854e-02
|
2020-02-02 12:27:35 Iteration 1100 Training Loss: 1.846e-01 Loss in Target Net: 4.211e-02
|
2020-02-02 12:27:53 Iteration 1150 Training Loss: 1.854e-01 Loss in Target Net: 4.158e-02
|
2020-02-02 12:28:12 Iteration 1200 Training Loss: 1.855e-01 Loss in Target Net: 3.489e-02
|
2020-02-02 12:28:31 Iteration 1250 Training Loss: 1.853e-01 Loss in Target Net: 3.293e-02
|
2020-02-02 12:28:50 Iteration 1300 Training Loss: 1.822e-01 Loss in Target Net: 4.178e-02
|
2020-02-02 12:29:10 Iteration 1350 Training Loss: 1.821e-01 Loss in Target Net: 3.649e-02
|
2020-02-02 12:29:29 Iteration 1400 Training Loss: 1.850e-01 Loss in Target Net: 3.752e-02
|
2020-02-02 12:29:48 Iteration 1450 Training Loss: 1.830e-01 Loss in Target Net: 3.355e-02
|
2020-02-02 12:30:06 Iteration 1499 Training Loss: 1.875e-01 Loss in Target Net: 3.168e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:30:16, Epoch 0, Iteration 7, loss 0.312 (0.364), acc 88.462 (92.800)
|
2020-02-02 12:31:14, 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:[-0.30783582, -0.834488, -2.63798, -0.061580427, -1.1717186, -3.249704, 8.149574, -2.1287951, 5.4853573, -2.9327822], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:32:13 Epoch 59, Val iteration 0, acc 92.400 (92.400)
|
2020-02-02 12:32:21 Epoch 59, Val iteration 19, acc 92.000 (92.900)
|
* Prec: 92.90000190734864
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 9
|
TARGET INDEX: 34
|
DPN92 0
|
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=35, 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/1500/35
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:18:32 Iteration 0 Training Loss: 1.026e+00 Loss in Target Net: 1.374e+00
|
2020-02-02 12:18:49 Iteration 50 Training Loss: 2.314e-01 Loss in Target Net: 5.204e-02
|
2020-02-02 12:19:07 Iteration 100 Training Loss: 2.021e-01 Loss in Target Net: 2.873e-02
|
2020-02-02 12:19:25 Iteration 150 Training Loss: 1.869e-01 Loss in Target Net: 2.185e-02
|
2020-02-02 12:19:43 Iteration 200 Training Loss: 1.859e-01 Loss in Target Net: 2.492e-02
|
2020-02-02 12:20:04 Iteration 250 Training Loss: 1.808e-01 Loss in Target Net: 1.709e-02
|
2020-02-02 12:20:24 Iteration 300 Training Loss: 1.806e-01 Loss in Target Net: 1.873e-02
|
2020-02-02 12:20:43 Iteration 350 Training Loss: 1.784e-01 Loss in Target Net: 1.821e-02
|
2020-02-02 12:21:04 Iteration 400 Training Loss: 1.763e-01 Loss in Target Net: 1.700e-02
|
2020-02-02 12:21:22 Iteration 450 Training Loss: 1.746e-01 Loss in Target Net: 1.852e-02
|
2020-02-02 12:21:40 Iteration 500 Training Loss: 1.768e-01 Loss in Target Net: 1.682e-02
|
2020-02-02 12:21:56 Iteration 550 Training Loss: 1.727e-01 Loss in Target Net: 1.881e-02
|
2020-02-02 12:22:16 Iteration 600 Training Loss: 1.759e-01 Loss in Target Net: 1.945e-02
|
2020-02-02 12:22:33 Iteration 650 Training Loss: 1.703e-01 Loss in Target Net: 1.544e-02
|
2020-02-02 12:22:52 Iteration 700 Training Loss: 1.696e-01 Loss in Target Net: 1.577e-02
|
2020-02-02 12:23:11 Iteration 750 Training Loss: 1.747e-01 Loss in Target Net: 1.742e-02
|
2020-02-02 12:23:31 Iteration 800 Training Loss: 1.694e-01 Loss in Target Net: 1.608e-02
|
2020-02-02 12:23:51 Iteration 850 Training Loss: 1.738e-01 Loss in Target Net: 1.903e-02
|
2020-02-02 12:24:10 Iteration 900 Training Loss: 1.664e-01 Loss in Target Net: 1.849e-02
|
2020-02-02 12:24:27 Iteration 950 Training Loss: 1.695e-01 Loss in Target Net: 1.820e-02
|
2020-02-02 12:24:46 Iteration 1000 Training Loss: 1.695e-01 Loss in Target Net: 1.648e-02
|
2020-02-02 12:25:04 Iteration 1050 Training Loss: 1.709e-01 Loss in Target Net: 1.666e-02
|
2020-02-02 12:25:21 Iteration 1100 Training Loss: 1.742e-01 Loss in Target Net: 1.330e-02
|
2020-02-02 12:25:38 Iteration 1150 Training Loss: 1.639e-01 Loss in Target Net: 1.874e-02
|
2020-02-02 12:25:56 Iteration 1200 Training Loss: 1.714e-01 Loss in Target Net: 1.883e-02
|
2020-02-02 12:26:15 Iteration 1250 Training Loss: 1.691e-01 Loss in Target Net: 1.659e-02
|
2020-02-02 12:26:34 Iteration 1300 Training Loss: 1.678e-01 Loss in Target Net: 1.881e-02
|
2020-02-02 12:26:52 Iteration 1350 Training Loss: 1.703e-01 Loss in Target Net: 1.715e-02
|
2020-02-02 12:27:10 Iteration 1400 Training Loss: 1.658e-01 Loss in Target Net: 1.759e-02
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.