text stringlengths 5 1.13k |
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2020-02-04 03:46:00 Iteration 1150 Training Loss: 1.644e-01 Loss in Target Net: 3.486e-02 |
2020-02-04 03:49:21 Iteration 1200 Training Loss: 1.610e-01 Loss in Target Net: 3.722e-02 |
2020-02-04 03:52:43 Iteration 1250 Training Loss: 1.616e-01 Loss in Target Net: 3.624e-02 |
2020-02-04 03:56:05 Iteration 1300 Training Loss: 1.650e-01 Loss in Target Net: 3.829e-02 |
2020-02-04 03:59:22 Iteration 1350 Training Loss: 1.619e-01 Loss in Target Net: 3.775e-02 |
2020-02-04 04:03:02 Iteration 1400 Training Loss: 1.627e-01 Loss in Target Net: 3.789e-02 |
2020-02-04 04:06:45 Iteration 1450 Training Loss: 1.667e-01 Loss in Target Net: 3.767e-02 |
2020-02-04 04:09:59 Iteration 1499 Training Loss: 1.659e-01 Loss in Target Net: 3.418e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:11:15, Epoch 0, Iteration 7, loss 0.345 (0.469), acc 86.538 (89.800) |
2020-02-04 04:16:25, 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:[-5.448987, 0.93121326, -1.3877081, -3.059204, -2.4522712, 0.49164057, 7.511086, -1.5146266, 9.528828, -4.141304], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:22:18 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-02-04 04:23:10 Epoch 59, Val iteration 19, acc 92.200 (93.010) |
* Prec: 93.01000099182129 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 101 |
TARGET INDEX: 25 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='10', 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=26, 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/1500/26 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:31:21 Iteration 0 Training Loss: 1.055e+00 Loss in Target Net: 1.447e+00 |
2020-02-04 02:34:48 Iteration 50 Training Loss: 2.401e-01 Loss in Target Net: 9.503e-02 |
2020-02-04 02:38:14 Iteration 100 Training Loss: 2.127e-01 Loss in Target Net: 7.702e-02 |
2020-02-04 02:41:37 Iteration 150 Training Loss: 2.035e-01 Loss in Target Net: 5.502e-02 |
2020-02-04 02:45:03 Iteration 200 Training Loss: 1.949e-01 Loss in Target Net: 5.682e-02 |
2020-02-04 02:48:29 Iteration 250 Training Loss: 1.973e-01 Loss in Target Net: 5.438e-02 |
2020-02-04 02:51:58 Iteration 300 Training Loss: 1.857e-01 Loss in Target Net: 4.206e-02 |
2020-02-04 02:55:23 Iteration 350 Training Loss: 1.812e-01 Loss in Target Net: 5.052e-02 |
2020-02-04 02:58:48 Iteration 400 Training Loss: 1.821e-01 Loss in Target Net: 4.798e-02 |
2020-02-04 03:02:12 Iteration 450 Training Loss: 1.823e-01 Loss in Target Net: 5.269e-02 |
2020-02-04 03:05:36 Iteration 500 Training Loss: 1.763e-01 Loss in Target Net: 6.223e-02 |
2020-02-04 03:09:03 Iteration 550 Training Loss: 1.787e-01 Loss in Target Net: 5.219e-02 |
2020-02-04 03:12:29 Iteration 600 Training Loss: 1.834e-01 Loss in Target Net: 4.502e-02 |
2020-02-04 03:15:53 Iteration 650 Training Loss: 1.721e-01 Loss in Target Net: 4.431e-02 |
2020-02-04 03:19:17 Iteration 700 Training Loss: 1.761e-01 Loss in Target Net: 5.090e-02 |
2020-02-04 03:22:41 Iteration 750 Training Loss: 1.732e-01 Loss in Target Net: 5.977e-02 |
2020-02-04 03:26:05 Iteration 800 Training Loss: 1.773e-01 Loss in Target Net: 6.087e-02 |
2020-02-04 03:29:29 Iteration 850 Training Loss: 1.735e-01 Loss in Target Net: 5.823e-02 |
2020-02-04 03:32:55 Iteration 900 Training Loss: 1.719e-01 Loss in Target Net: 4.575e-02 |
2020-02-04 03:36:20 Iteration 950 Training Loss: 1.742e-01 Loss in Target Net: 7.660e-02 |
2020-02-04 03:39:44 Iteration 1000 Training Loss: 1.709e-01 Loss in Target Net: 6.394e-02 |
2020-02-04 03:43:10 Iteration 1050 Training Loss: 1.715e-01 Loss in Target Net: 4.722e-02 |
2020-02-04 03:46:35 Iteration 1100 Training Loss: 1.743e-01 Loss in Target Net: 4.306e-02 |
2020-02-04 03:49:59 Iteration 1150 Training Loss: 1.805e-01 Loss in Target Net: 6.661e-02 |
2020-02-04 03:53:23 Iteration 1200 Training Loss: 1.742e-01 Loss in Target Net: 5.743e-02 |
2020-02-04 03:56:47 Iteration 1250 Training Loss: 1.750e-01 Loss in Target Net: 4.021e-02 |
2020-02-04 04:00:11 Iteration 1300 Training Loss: 1.690e-01 Loss in Target Net: 4.526e-02 |
2020-02-04 04:03:57 Iteration 1350 Training Loss: 1.681e-01 Loss in Target Net: 5.815e-02 |
2020-02-04 04:07:41 Iteration 1400 Training Loss: 1.719e-01 Loss in Target Net: 5.405e-02 |
2020-02-04 04:10:53 Iteration 1450 Training Loss: 1.728e-01 Loss in Target Net: 5.088e-02 |
2020-02-04 04:14:11 Iteration 1499 Training Loss: 1.701e-01 Loss in Target Net: 5.389e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 04:15:08, Epoch 0, Iteration 7, loss 0.170 (0.408), acc 96.154 (90.600) |
2020-02-04 04:20:31, 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:[-1.0158284, -0.3631082, -2.3883429, -0.4365751, -1.7680184, -2.350724, 8.985095, -2.4081135, 4.426524, -2.2864637], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 04:26:05 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-02-04 04:26:57 Epoch 59, Val iteration 19, acc 92.600 (92.430) |
* Prec: 92.43000106811523 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 103 |
TARGET INDEX: 26 |
DPN92 0 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='11', 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=27, 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/1500/27 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 02:28:12 Iteration 0 Training Loss: 9.881e-01 Loss in Target Net: 1.319e+00 |
2020-02-04 02:31:28 Iteration 50 Training Loss: 2.364e-01 Loss in Target Net: 4.556e-02 |
2020-02-04 02:34:51 Iteration 100 Training Loss: 1.987e-01 Loss in Target Net: 4.823e-02 |
2020-02-04 02:38:14 Iteration 150 Training Loss: 1.853e-01 Loss in Target Net: 4.444e-02 |
2020-02-04 02:41:35 Iteration 200 Training Loss: 1.758e-01 Loss in Target Net: 3.961e-02 |
2020-02-04 02:44:55 Iteration 250 Training Loss: 1.736e-01 Loss in Target Net: 3.659e-02 |
2020-02-04 02:48:16 Iteration 300 Training Loss: 1.688e-01 Loss in Target Net: 3.659e-02 |
2020-02-04 02:51:36 Iteration 350 Training Loss: 1.677e-01 Loss in Target Net: 3.317e-02 |
2020-02-04 02:54:56 Iteration 400 Training Loss: 1.656e-01 Loss in Target Net: 3.554e-02 |
2020-02-04 02:58:17 Iteration 450 Training Loss: 1.621e-01 Loss in Target Net: 3.590e-02 |
2020-02-04 03:01:38 Iteration 500 Training Loss: 1.629e-01 Loss in Target Net: 3.474e-02 |
2020-02-04 03:04:58 Iteration 550 Training Loss: 1.603e-01 Loss in Target Net: 3.345e-02 |
2020-02-04 03:08:19 Iteration 600 Training Loss: 1.594e-01 Loss in Target Net: 3.775e-02 |
2020-02-04 03:11:40 Iteration 650 Training Loss: 1.596e-01 Loss in Target Net: 3.215e-02 |
2020-02-04 03:15:00 Iteration 700 Training Loss: 1.586e-01 Loss in Target Net: 3.909e-02 |
2020-02-04 03:18:22 Iteration 750 Training Loss: 1.564e-01 Loss in Target Net: 3.303e-02 |
2020-02-04 03:21:44 Iteration 800 Training Loss: 1.584e-01 Loss in Target Net: 3.344e-02 |
2020-02-04 03:25:06 Iteration 850 Training Loss: 1.573e-01 Loss in Target Net: 3.381e-02 |
2020-02-04 03:28:27 Iteration 900 Training Loss: 1.576e-01 Loss in Target Net: 3.748e-02 |
2020-02-04 03:31:47 Iteration 950 Training Loss: 1.575e-01 Loss in Target Net: 3.369e-02 |
2020-02-04 03:35:09 Iteration 1000 Training Loss: 1.550e-01 Loss in Target Net: 3.390e-02 |
2020-02-04 03:38:30 Iteration 1050 Training Loss: 1.568e-01 Loss in Target Net: 3.449e-02 |
2020-02-04 03:41:51 Iteration 1100 Training Loss: 1.551e-01 Loss in Target Net: 3.606e-02 |
2020-02-04 03:45:11 Iteration 1150 Training Loss: 1.540e-01 Loss in Target Net: 3.362e-02 |
2020-02-04 03:48:33 Iteration 1200 Training Loss: 1.564e-01 Loss in Target Net: 3.606e-02 |
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