text stringlengths 5 1.13k |
|---|
2020-02-04 03:23:23 Iteration 950 Training Loss: 1.406e-01 Loss in Target Net: 2.015e-02 |
2020-02-04 03:26:35 Iteration 1000 Training Loss: 1.409e-01 Loss in Target Net: 2.005e-02 |
2020-02-04 03:29:48 Iteration 1050 Training Loss: 1.397e-01 Loss in Target Net: 2.314e-02 |
2020-02-04 03:33:01 Iteration 1100 Training Loss: 1.411e-01 Loss in Target Net: 2.285e-02 |
2020-02-04 03:36:14 Iteration 1150 Training Loss: 1.409e-01 Loss in Target Net: 2.131e-02 |
2020-02-04 03:39:29 Iteration 1200 Training Loss: 1.401e-01 Loss in Target Net: 2.133e-02 |
2020-02-04 03:42:41 Iteration 1250 Training Loss: 1.397e-01 Loss in Target Net: 2.270e-02 |
2020-02-04 03:45:59 Iteration 1300 Training Loss: 1.401e-01 Loss in Target Net: 2.091e-02 |
2020-02-04 03:49:17 Iteration 1350 Training Loss: 1.397e-01 Loss in Target Net: 2.116e-02 |
2020-02-04 03:52:33 Iteration 1400 Training Loss: 1.387e-01 Loss in Target Net: 2.220e-02 |
2020-02-04 03:55:49 Iteration 1450 Training Loss: 1.389e-01 Loss in Target Net: 2.068e-02 |
2020-02-04 03:59:01 Iteration 1499 Training Loss: 1.414e-01 Loss in Target Net: 2.114e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:00:03, Epoch 0, Iteration 7, loss 0.278 (0.451), acc 90.385 (89.400) |
2020-02-04 04:05:05, 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:[-3.79601, -1.1834717, -1.7837374, -1.765379, -1.1487181, -1.7654587, 5.526844, -1.3327838, 9.061089, -1.4529016], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:10:45 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-02-04 04:11:34 Epoch 59, Val iteration 19, acc 92.000 (93.230) |
* Prec: 93.23000068664551 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
TARGET INDEX: 21 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='6', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=22, 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-3Repeat/1500/22 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:30:13 Iteration 0 Training Loss: 9.814e-01 Loss in Target Net: 1.299e+00 |
2020-02-04 02:33:45 Iteration 50 Training Loss: 2.180e-01 Loss in Target Net: 5.370e-02 |
2020-02-04 02:37:14 Iteration 100 Training Loss: 1.873e-01 Loss in Target Net: 3.295e-02 |
2020-02-04 02:40:40 Iteration 150 Training Loss: 1.750e-01 Loss in Target Net: 2.595e-02 |
2020-02-04 02:44:05 Iteration 200 Training Loss: 1.656e-01 Loss in Target Net: 2.707e-02 |
2020-02-04 02:47:31 Iteration 250 Training Loss: 1.653e-01 Loss in Target Net: 2.689e-02 |
2020-02-04 02:51:00 Iteration 300 Training Loss: 1.620e-01 Loss in Target Net: 2.455e-02 |
2020-02-04 02:54:25 Iteration 350 Training Loss: 1.576e-01 Loss in Target Net: 2.581e-02 |
2020-02-04 02:57:54 Iteration 400 Training Loss: 1.548e-01 Loss in Target Net: 2.525e-02 |
2020-02-04 03:01:24 Iteration 450 Training Loss: 1.548e-01 Loss in Target Net: 2.445e-02 |
2020-02-04 03:04:51 Iteration 500 Training Loss: 1.553e-01 Loss in Target Net: 2.406e-02 |
2020-02-04 03:08:17 Iteration 550 Training Loss: 1.546e-01 Loss in Target Net: 2.391e-02 |
2020-02-04 03:11:47 Iteration 600 Training Loss: 1.535e-01 Loss in Target Net: 2.345e-02 |
2020-02-04 03:15:17 Iteration 650 Training Loss: 1.517e-01 Loss in Target Net: 2.500e-02 |
2020-02-04 03:18:44 Iteration 700 Training Loss: 1.533e-01 Loss in Target Net: 2.285e-02 |
2020-02-04 03:22:09 Iteration 750 Training Loss: 1.496e-01 Loss in Target Net: 2.800e-02 |
2020-02-04 03:25:35 Iteration 800 Training Loss: 1.502e-01 Loss in Target Net: 2.665e-02 |
2020-02-04 03:28:59 Iteration 850 Training Loss: 1.506e-01 Loss in Target Net: 2.343e-02 |
2020-02-04 03:32:25 Iteration 900 Training Loss: 1.496e-01 Loss in Target Net: 3.085e-02 |
2020-02-04 03:35:50 Iteration 950 Training Loss: 1.504e-01 Loss in Target Net: 3.100e-02 |
2020-02-04 03:39:21 Iteration 1000 Training Loss: 1.501e-01 Loss in Target Net: 2.846e-02 |
2020-02-04 03:42:52 Iteration 1050 Training Loss: 1.490e-01 Loss in Target Net: 2.872e-02 |
2020-02-04 03:46:18 Iteration 1100 Training Loss: 1.499e-01 Loss in Target Net: 2.580e-02 |
2020-02-04 03:49:45 Iteration 1150 Training Loss: 1.482e-01 Loss in Target Net: 2.655e-02 |
2020-02-04 03:53:15 Iteration 1200 Training Loss: 1.497e-01 Loss in Target Net: 2.559e-02 |
2020-02-04 03:56:46 Iteration 1250 Training Loss: 1.464e-01 Loss in Target Net: 2.178e-02 |
2020-02-04 04:00:13 Iteration 1300 Training Loss: 1.504e-01 Loss in Target Net: 2.422e-02 |
2020-02-04 04:04:04 Iteration 1350 Training Loss: 1.500e-01 Loss in Target Net: 2.582e-02 |
2020-02-04 04:07:50 Iteration 1400 Training Loss: 1.488e-01 Loss in Target Net: 2.702e-02 |
2020-02-04 04:11:09 Iteration 1450 Training Loss: 1.491e-01 Loss in Target Net: 2.112e-02 |
2020-02-04 04:14:35 Iteration 1499 Training Loss: 1.480e-01 Loss in Target Net: 2.419e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:15:33, Epoch 0, Iteration 7, loss 0.469 (0.643), acc 88.462 (87.200) |
2020-02-04 04:20:58, 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:[-3.4513063, -0.20221725, -2.572649, -3.0944858, -0.9151263, -4.083639, 8.561004, -2.1368942, 9.13083, -0.84219915], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:26:32 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-02-04 04:27:24 Epoch 59, Val iteration 19, acc 92.400 (93.150) |
* Prec: 93.15000190734864 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 105 |
TARGET INDEX: 22 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='7', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=23, 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-3Repeat/1500/23 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:27:21 Iteration 0 Training Loss: 9.988e-01 Loss in Target Net: 1.282e+00 |
2020-02-04 02:30:32 Iteration 50 Training Loss: 2.020e-01 Loss in Target Net: 3.786e-02 |
2020-02-04 02:34:00 Iteration 100 Training Loss: 1.706e-01 Loss in Target Net: 3.090e-02 |
2020-02-04 02:37:27 Iteration 150 Training Loss: 1.587e-01 Loss in Target Net: 3.032e-02 |
2020-02-04 02:40:51 Iteration 200 Training Loss: 1.524e-01 Loss in Target Net: 3.113e-02 |
2020-02-04 02:44:13 Iteration 250 Training Loss: 1.501e-01 Loss in Target Net: 2.783e-02 |
2020-02-04 02:47:35 Iteration 300 Training Loss: 1.466e-01 Loss in Target Net: 2.578e-02 |
2020-02-04 02:50:58 Iteration 350 Training Loss: 1.433e-01 Loss in Target Net: 2.730e-02 |
2020-02-04 02:54:20 Iteration 400 Training Loss: 1.434e-01 Loss in Target Net: 2.330e-02 |
2020-02-04 02:57:47 Iteration 450 Training Loss: 1.416e-01 Loss in Target Net: 2.364e-02 |
2020-02-04 03:01:13 Iteration 500 Training Loss: 1.397e-01 Loss in Target Net: 2.381e-02 |
2020-02-04 03:04:38 Iteration 550 Training Loss: 1.419e-01 Loss in Target Net: 2.162e-02 |
2020-02-04 03:08:03 Iteration 600 Training Loss: 1.391e-01 Loss in Target Net: 2.385e-02 |
2020-02-04 03:11:26 Iteration 650 Training Loss: 1.397e-01 Loss in Target Net: 2.271e-02 |
2020-02-04 03:14:49 Iteration 700 Training Loss: 1.384e-01 Loss in Target Net: 2.347e-02 |
2020-02-04 03:18:15 Iteration 750 Training Loss: 1.383e-01 Loss in Target Net: 2.311e-02 |
2020-02-04 03:21:38 Iteration 800 Training Loss: 1.384e-01 Loss in Target Net: 2.429e-02 |
2020-02-04 03:25:01 Iteration 850 Training Loss: 1.390e-01 Loss in Target Net: 2.472e-02 |
2020-02-04 03:28:24 Iteration 900 Training Loss: 1.359e-01 Loss in Target Net: 2.377e-02 |
2020-02-04 03:31:45 Iteration 950 Training Loss: 1.341e-01 Loss in Target Net: 2.249e-02 |
2020-02-04 03:35:08 Iteration 1000 Training Loss: 1.394e-01 Loss in Target Net: 2.408e-02 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.