text
stringlengths 5
1.13k
|
|---|
2020-02-02 12:27:29 Iteration 1450 Training Loss: 1.697e-01 Loss in Target Net: 1.909e-02
|
2020-02-02 12:27:46 Iteration 1499 Training Loss: 1.725e-01 Loss in Target Net: 2.082e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:27:56, Epoch 0, Iteration 7, loss 0.853 (0.398), acc 78.846 (90.800)
|
2020-02-02 12:28:53, 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:[-3.2905629, -0.61401814, -2.557784, -0.8111481, -0.74326193, -0.6141568, 8.765216, -2.3591757, 6.3316135, -3.918728], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:29:53 Epoch 59, Val iteration 0, acc 93.400 (93.400)
|
2020-02-02 12:30:01 Epoch 59, Val iteration 19, acc 91.800 (92.770)
|
* Prec: 92.77000160217285
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 9
|
TARGET INDEX: 35
|
DPN92 0
|
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=36, 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/1500/36
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:32:19 Iteration 0 Training Loss: 1.043e+00 Loss in Target Net: 1.414e+00
|
2020-02-02 12:32:38 Iteration 50 Training Loss: 2.539e-01 Loss in Target Net: 6.979e-02
|
2020-02-02 12:32:56 Iteration 100 Training Loss: 2.268e-01 Loss in Target Net: 8.764e-02
|
2020-02-02 12:33:13 Iteration 150 Training Loss: 2.113e-01 Loss in Target Net: 7.931e-02
|
2020-02-02 12:33:30 Iteration 200 Training Loss: 2.131e-01 Loss in Target Net: 8.116e-02
|
2020-02-02 12:33:48 Iteration 250 Training Loss: 2.050e-01 Loss in Target Net: 9.072e-02
|
2020-02-02 12:34:06 Iteration 300 Training Loss: 1.977e-01 Loss in Target Net: 1.169e-01
|
2020-02-02 12:34:23 Iteration 350 Training Loss: 2.044e-01 Loss in Target Net: 6.380e-02
|
2020-02-02 12:34:40 Iteration 400 Training Loss: 1.999e-01 Loss in Target Net: 7.170e-02
|
2020-02-02 12:34:58 Iteration 450 Training Loss: 1.986e-01 Loss in Target Net: 6.320e-02
|
2020-02-02 12:35:17 Iteration 500 Training Loss: 1.927e-01 Loss in Target Net: 7.019e-02
|
2020-02-02 12:35:34 Iteration 550 Training Loss: 1.982e-01 Loss in Target Net: 7.779e-02
|
2020-02-02 12:35:53 Iteration 600 Training Loss: 1.944e-01 Loss in Target Net: 4.000e-02
|
2020-02-02 12:36:11 Iteration 650 Training Loss: 1.932e-01 Loss in Target Net: 5.856e-02
|
2020-02-02 12:36:30 Iteration 700 Training Loss: 1.912e-01 Loss in Target Net: 4.734e-02
|
2020-02-02 12:36:47 Iteration 750 Training Loss: 1.933e-01 Loss in Target Net: 7.574e-02
|
2020-02-02 12:37:06 Iteration 800 Training Loss: 1.924e-01 Loss in Target Net: 7.275e-02
|
2020-02-02 12:37:23 Iteration 850 Training Loss: 1.912e-01 Loss in Target Net: 5.219e-02
|
2020-02-02 12:37:41 Iteration 900 Training Loss: 1.879e-01 Loss in Target Net: 5.662e-02
|
2020-02-02 12:38:00 Iteration 950 Training Loss: 1.922e-01 Loss in Target Net: 4.351e-02
|
2020-02-02 12:38:17 Iteration 1000 Training Loss: 1.882e-01 Loss in Target Net: 8.570e-02
|
2020-02-02 12:38:35 Iteration 1050 Training Loss: 1.929e-01 Loss in Target Net: 4.535e-02
|
2020-02-02 12:38:53 Iteration 1100 Training Loss: 1.853e-01 Loss in Target Net: 6.371e-02
|
2020-02-02 12:39:12 Iteration 1150 Training Loss: 1.892e-01 Loss in Target Net: 8.498e-02
|
2020-02-02 12:39:30 Iteration 1200 Training Loss: 1.858e-01 Loss in Target Net: 5.683e-02
|
2020-02-02 12:39:50 Iteration 1250 Training Loss: 1.855e-01 Loss in Target Net: 5.987e-02
|
2020-02-02 12:40:10 Iteration 1300 Training Loss: 1.880e-01 Loss in Target Net: 6.630e-02
|
2020-02-02 12:40:30 Iteration 1350 Training Loss: 1.925e-01 Loss in Target Net: 6.644e-02
|
2020-02-02 12:40:49 Iteration 1400 Training Loss: 1.834e-01 Loss in Target Net: 5.668e-02
|
2020-02-02 12:41:08 Iteration 1450 Training Loss: 1.863e-01 Loss in Target Net: 9.580e-02
|
2020-02-02 12:41:29 Iteration 1499 Training Loss: 1.781e-01 Loss in Target Net: 8.379e-02
|
Evaluating against victims networks
|
DPN92
|
Using Adam for retraining
|
Files already downloaded and verified
|
2020-02-02 12:41:38, Epoch 0, Iteration 7, loss 0.319 (0.418), acc 88.462 (91.200)
|
2020-02-02 12:42:37, 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:[-6.382359, -2.4818008, -0.79729766, 4.984265, 1.3064785, -1.4904854, 8.406157, -3.924132, 3.6243463, -3.0438159], Poisons' Predictions:[8, 8, 8, 8, 8]
|
2020-02-02 12:43:37 Epoch 59, Val iteration 0, acc 92.400 (92.400)
|
2020-02-02 12:43:44 Epoch 59, Val iteration 19, acc 92.000 (92.430)
|
* Prec: 92.43000068664551
|
--------
|
------SUMMARY------
|
TIME ELAPSED (mins): 9
|
TARGET INDEX: 36
|
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=37, 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/1500/37
|
Selected base image indices: [213, 225, 227, 247, 249]
|
2020-02-02 12:33:31 Iteration 0 Training Loss: 9.848e-01 Loss in Target Net: 1.345e+00
|
2020-02-02 12:33:51 Iteration 50 Training Loss: 2.158e-01 Loss in Target Net: 3.782e-02
|
2020-02-02 12:34:11 Iteration 100 Training Loss: 1.933e-01 Loss in Target Net: 3.346e-02
|
2020-02-02 12:34:30 Iteration 150 Training Loss: 1.818e-01 Loss in Target Net: 3.325e-02
|
2020-02-02 12:34:47 Iteration 200 Training Loss: 1.807e-01 Loss in Target Net: 3.596e-02
|
2020-02-02 12:35:04 Iteration 250 Training Loss: 1.726e-01 Loss in Target Net: 3.226e-02
|
2020-02-02 12:35:20 Iteration 300 Training Loss: 1.679e-01 Loss in Target Net: 3.134e-02
|
2020-02-02 12:35:39 Iteration 350 Training Loss: 1.654e-01 Loss in Target Net: 3.213e-02
|
2020-02-02 12:35:56 Iteration 400 Training Loss: 1.665e-01 Loss in Target Net: 2.857e-02
|
2020-02-02 12:36:14 Iteration 450 Training Loss: 1.709e-01 Loss in Target Net: 2.847e-02
|
2020-02-02 12:36:32 Iteration 500 Training Loss: 1.646e-01 Loss in Target Net: 2.194e-02
|
2020-02-02 12:36:51 Iteration 550 Training Loss: 1.656e-01 Loss in Target Net: 2.701e-02
|
2020-02-02 12:37:09 Iteration 600 Training Loss: 1.673e-01 Loss in Target Net: 2.276e-02
|
2020-02-02 12:37:25 Iteration 650 Training Loss: 1.677e-01 Loss in Target Net: 2.238e-02
|
2020-02-02 12:37:43 Iteration 700 Training Loss: 1.606e-01 Loss in Target Net: 2.635e-02
|
2020-02-02 12:38:02 Iteration 750 Training Loss: 1.611e-01 Loss in Target Net: 2.390e-02
|
2020-02-02 12:38:21 Iteration 800 Training Loss: 1.620e-01 Loss in Target Net: 2.336e-02
|
2020-02-02 12:38:41 Iteration 850 Training Loss: 1.650e-01 Loss in Target Net: 2.029e-02
|
2020-02-02 12:38:59 Iteration 900 Training Loss: 1.601e-01 Loss in Target Net: 2.321e-02
|
2020-02-02 12:39:18 Iteration 950 Training Loss: 1.584e-01 Loss in Target Net: 2.258e-02
|
2020-02-02 12:39:38 Iteration 1000 Training Loss: 1.581e-01 Loss in Target Net: 2.596e-02
|
2020-02-02 12:39:56 Iteration 1050 Training Loss: 1.613e-01 Loss in Target Net: 2.235e-02
|
2020-02-02 12:40:16 Iteration 1100 Training Loss: 1.621e-01 Loss in Target Net: 2.227e-02
|
2020-02-02 12:40:35 Iteration 1150 Training Loss: 1.644e-01 Loss in Target Net: 2.118e-02
|
2020-02-02 12:40:55 Iteration 1200 Training Loss: 1.610e-01 Loss in Target Net: 2.668e-02
|
2020-02-02 12:41:13 Iteration 1250 Training Loss: 1.610e-01 Loss in Target Net: 1.970e-02
|
2020-02-02 12:41:32 Iteration 1300 Training Loss: 1.616e-01 Loss in Target Net: 2.458e-02
|
2020-02-02 12:41:51 Iteration 1350 Training Loss: 1.653e-01 Loss in Target Net: 2.231e-02
|
2020-02-02 12:42:08 Iteration 1400 Training Loss: 1.592e-01 Loss in Target Net: 2.393e-02
|
2020-02-02 12:42:25 Iteration 1450 Training Loss: 1.569e-01 Loss in Target Net: 2.512e-02
|
2020-02-02 12:42:43 Iteration 1499 Training Loss: 1.611e-01 Loss in Target Net: 2.149e-02
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.