text stringlengths 5 1.13k |
|---|
2020-02-04 01:26:45 Iteration 850 Training Loss: 1.598e-01 Loss in Target Net: 3.759e-02 |
2020-02-04 01:29:58 Iteration 900 Training Loss: 1.560e-01 Loss in Target Net: 4.002e-02 |
2020-02-04 01:33:10 Iteration 950 Training Loss: 1.572e-01 Loss in Target Net: 3.440e-02 |
2020-02-04 01:36:23 Iteration 1000 Training Loss: 1.549e-01 Loss in Target Net: 4.120e-02 |
2020-02-04 01:39:35 Iteration 1050 Training Loss: 1.548e-01 Loss in Target Net: 3.813e-02 |
2020-02-04 01:42:47 Iteration 1100 Training Loss: 1.557e-01 Loss in Target Net: 4.806e-02 |
2020-02-04 01:45:58 Iteration 1150 Training Loss: 1.527e-01 Loss in Target Net: 3.856e-02 |
2020-02-04 01:49:11 Iteration 1200 Training Loss: 1.540e-01 Loss in Target Net: 3.608e-02 |
2020-02-04 01:52:23 Iteration 1250 Training Loss: 1.530e-01 Loss in Target Net: 4.134e-02 |
2020-02-04 01:55:34 Iteration 1300 Training Loss: 1.560e-01 Loss in Target Net: 3.862e-02 |
2020-02-04 01:58:46 Iteration 1350 Training Loss: 1.546e-01 Loss in Target Net: 3.649e-02 |
2020-02-04 02:01:58 Iteration 1400 Training Loss: 1.512e-01 Loss in Target Net: 4.054e-02 |
2020-02-04 02:05:09 Iteration 1450 Training Loss: 1.516e-01 Loss in Target Net: 4.297e-02 |
2020-02-04 02:08:16 Iteration 1499 Training Loss: 1.548e-01 Loss in Target Net: 3.845e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:09:27, Epoch 0, Iteration 7, loss 0.410 (0.516), acc 88.462 (89.400) |
2020-02-04 02:14:25, 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:[-0.2938413, -0.5591924, -1.5646396, -0.42753863, -1.4285426, -3.6135466, 4.850048, -2.224141, 6.6643963, -1.1585144], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:19:54 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 02:20:44 Epoch 59, Val iteration 19, acc 93.800 (93.380) |
* Prec: 93.38000221252442 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 96 |
TARGET INDEX: 2 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='4', 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=20, 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/1500/20 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:21:12 Iteration 0 Training Loss: 1.014e+00 Loss in Target Net: 1.236e+00 |
2020-02-04 02:24:34 Iteration 50 Training Loss: 2.376e-01 Loss in Target Net: 5.895e-02 |
2020-02-04 02:27:51 Iteration 100 Training Loss: 1.995e-01 Loss in Target Net: 4.579e-02 |
2020-02-04 02:30:55 Iteration 150 Training Loss: 1.820e-01 Loss in Target Net: 3.467e-02 |
2020-02-04 02:34:09 Iteration 200 Training Loss: 1.762e-01 Loss in Target Net: 3.184e-02 |
2020-02-04 02:37:24 Iteration 250 Training Loss: 1.705e-01 Loss in Target Net: 2.983e-02 |
2020-02-04 02:40:39 Iteration 300 Training Loss: 1.669e-01 Loss in Target Net: 3.070e-02 |
2020-02-04 02:43:52 Iteration 350 Training Loss: 1.646e-01 Loss in Target Net: 3.444e-02 |
2020-02-04 02:47:09 Iteration 400 Training Loss: 1.634e-01 Loss in Target Net: 3.117e-02 |
2020-02-04 02:50:24 Iteration 450 Training Loss: 1.632e-01 Loss in Target Net: 3.185e-02 |
2020-02-04 02:53:40 Iteration 500 Training Loss: 1.599e-01 Loss in Target Net: 3.160e-02 |
2020-02-04 02:56:55 Iteration 550 Training Loss: 1.596e-01 Loss in Target Net: 3.415e-02 |
2020-02-04 03:00:11 Iteration 600 Training Loss: 1.596e-01 Loss in Target Net: 3.233e-02 |
2020-02-04 03:03:26 Iteration 650 Training Loss: 1.572e-01 Loss in Target Net: 3.306e-02 |
2020-02-04 03:06:42 Iteration 700 Training Loss: 1.561e-01 Loss in Target Net: 3.439e-02 |
2020-02-04 03:09:58 Iteration 750 Training Loss: 1.546e-01 Loss in Target Net: 3.649e-02 |
2020-02-04 03:13:14 Iteration 800 Training Loss: 1.564e-01 Loss in Target Net: 2.960e-02 |
2020-02-04 03:16:27 Iteration 850 Training Loss: 1.545e-01 Loss in Target Net: 3.217e-02 |
2020-02-04 03:19:41 Iteration 900 Training Loss: 1.560e-01 Loss in Target Net: 3.205e-02 |
2020-02-04 03:22:57 Iteration 950 Training Loss: 1.543e-01 Loss in Target Net: 3.265e-02 |
2020-02-04 03:26:13 Iteration 1000 Training Loss: 1.549e-01 Loss in Target Net: 3.337e-02 |
2020-02-04 03:29:29 Iteration 1050 Training Loss: 1.538e-01 Loss in Target Net: 3.466e-02 |
2020-02-04 03:32:42 Iteration 1100 Training Loss: 1.551e-01 Loss in Target Net: 3.315e-02 |
2020-02-04 03:35:56 Iteration 1150 Training Loss: 1.553e-01 Loss in Target Net: 3.407e-02 |
2020-02-04 03:39:08 Iteration 1200 Training Loss: 1.548e-01 Loss in Target Net: 3.380e-02 |
2020-02-04 03:42:20 Iteration 1250 Training Loss: 1.519e-01 Loss in Target Net: 3.336e-02 |
2020-02-04 03:45:32 Iteration 1300 Training Loss: 1.521e-01 Loss in Target Net: 3.152e-02 |
2020-02-04 03:48:49 Iteration 1350 Training Loss: 1.515e-01 Loss in Target Net: 3.325e-02 |
2020-02-04 03:52:06 Iteration 1400 Training Loss: 1.532e-01 Loss in Target Net: 3.315e-02 |
2020-02-04 03:55:19 Iteration 1450 Training Loss: 1.531e-01 Loss in Target Net: 2.960e-02 |
2020-02-04 03:58:25 Iteration 1499 Training Loss: 1.535e-01 Loss in Target Net: 3.117e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 03:59:33, Epoch 0, Iteration 7, loss 0.699 (0.513), acc 78.846 (88.800) |
2020-02-04 04:04:34, 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:[-1.7004989, 2.0430467, -2.456066, -2.440555, -1.9669017, -2.2844634, 6.878751, -2.9971094, 8.076338, -2.991687], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:10:11 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 04:11:01 Epoch 59, Val iteration 19, acc 92.800 (92.760) |
* Prec: 92.76000137329102 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 98 |
TARGET INDEX: 20 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='5', 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=21, 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/1500/21 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:21:48 Iteration 0 Training Loss: 1.041e+00 Loss in Target Net: 1.417e+00 |
2020-02-04 02:25:14 Iteration 50 Training Loss: 2.114e-01 Loss in Target Net: 5.419e-02 |
2020-02-04 02:28:28 Iteration 100 Training Loss: 1.827e-01 Loss in Target Net: 4.957e-02 |
2020-02-04 02:31:37 Iteration 150 Training Loss: 1.655e-01 Loss in Target Net: 4.313e-02 |
2020-02-04 02:34:52 Iteration 200 Training Loss: 1.583e-01 Loss in Target Net: 3.997e-02 |
2020-02-04 02:38:06 Iteration 250 Training Loss: 1.529e-01 Loss in Target Net: 2.854e-02 |
2020-02-04 02:41:19 Iteration 300 Training Loss: 1.512e-01 Loss in Target Net: 2.938e-02 |
2020-02-04 02:44:34 Iteration 350 Training Loss: 1.506e-01 Loss in Target Net: 2.880e-02 |
2020-02-04 02:47:47 Iteration 400 Training Loss: 1.507e-01 Loss in Target Net: 2.456e-02 |
2020-02-04 02:51:02 Iteration 450 Training Loss: 1.458e-01 Loss in Target Net: 2.702e-02 |
2020-02-04 02:54:18 Iteration 500 Training Loss: 1.453e-01 Loss in Target Net: 2.575e-02 |
2020-02-04 02:57:33 Iteration 550 Training Loss: 1.427e-01 Loss in Target Net: 2.581e-02 |
2020-02-04 03:00:46 Iteration 600 Training Loss: 1.487e-01 Loss in Target Net: 2.259e-02 |
2020-02-04 03:04:00 Iteration 650 Training Loss: 1.464e-01 Loss in Target Net: 2.413e-02 |
2020-02-04 03:07:14 Iteration 700 Training Loss: 1.439e-01 Loss in Target Net: 2.313e-02 |
2020-02-04 03:10:28 Iteration 750 Training Loss: 1.436e-01 Loss in Target Net: 2.264e-02 |
2020-02-04 03:13:43 Iteration 800 Training Loss: 1.440e-01 Loss in Target Net: 2.348e-02 |
2020-02-04 03:16:57 Iteration 850 Training Loss: 1.417e-01 Loss in Target Net: 2.135e-02 |
2020-02-04 03:20:09 Iteration 900 Training Loss: 1.425e-01 Loss in Target Net: 2.027e-02 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.