text stringlengths 5 1.13k |
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Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 05:43:50, Epoch 0, Iteration 7, loss 0.662 (0.404), acc 80.769 (90.600) |
2020-02-04 05:48:39, 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:[-2.6192408, -2.0201435, -2.8887684, 0.65329957, -1.9642566, -1.9416506, 4.143375, -1.0980197, 9.183909, -1.2235769], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 05:53:56 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-02-04 05:54:47 Epoch 59, Val iteration 19, acc 92.800 (93.180) |
* Prec: 93.18000221252441 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 93 |
TARGET INDEX: 34 |
DPN92 1 |
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=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio... |
Path: chk-black-end2end/mean-3Repeat/1500/35 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:11:37 Iteration 0 Training Loss: 1.023e+00 Loss in Target Net: 1.342e+00 |
2020-02-04 04:14:50 Iteration 50 Training Loss: 2.117e-01 Loss in Target Net: 4.657e-02 |
2020-02-04 04:18:13 Iteration 100 Training Loss: 1.791e-01 Loss in Target Net: 3.994e-02 |
2020-02-04 04:21:42 Iteration 150 Training Loss: 1.697e-01 Loss in Target Net: 2.662e-02 |
2020-02-04 04:25:00 Iteration 200 Training Loss: 1.653e-01 Loss in Target Net: 2.775e-02 |
2020-02-04 04:28:09 Iteration 250 Training Loss: 1.581e-01 Loss in Target Net: 2.329e-02 |
2020-02-04 04:31:20 Iteration 300 Training Loss: 1.581e-01 Loss in Target Net: 2.194e-02 |
2020-02-04 04:34:33 Iteration 350 Training Loss: 1.550e-01 Loss in Target Net: 2.653e-02 |
2020-02-04 04:37:46 Iteration 400 Training Loss: 1.535e-01 Loss in Target Net: 2.260e-02 |
2020-02-04 04:40:58 Iteration 450 Training Loss: 1.514e-01 Loss in Target Net: 2.191e-02 |
2020-02-04 04:44:11 Iteration 500 Training Loss: 1.556e-01 Loss in Target Net: 1.987e-02 |
2020-02-04 04:47:25 Iteration 550 Training Loss: 1.539e-01 Loss in Target Net: 2.173e-02 |
2020-02-04 04:50:38 Iteration 600 Training Loss: 1.512e-01 Loss in Target Net: 2.125e-02 |
2020-02-04 04:53:50 Iteration 650 Training Loss: 1.514e-01 Loss in Target Net: 1.878e-02 |
2020-02-04 04:57:04 Iteration 700 Training Loss: 1.519e-01 Loss in Target Net: 1.833e-02 |
2020-02-04 05:00:19 Iteration 750 Training Loss: 1.495e-01 Loss in Target Net: 2.097e-02 |
2020-02-04 05:03:32 Iteration 800 Training Loss: 1.466e-01 Loss in Target Net: 1.748e-02 |
2020-02-04 05:06:45 Iteration 850 Training Loss: 1.494e-01 Loss in Target Net: 2.004e-02 |
2020-02-04 05:09:59 Iteration 900 Training Loss: 1.478e-01 Loss in Target Net: 1.981e-02 |
2020-02-04 05:13:14 Iteration 950 Training Loss: 1.465e-01 Loss in Target Net: 2.011e-02 |
2020-02-04 05:16:25 Iteration 1000 Training Loss: 1.491e-01 Loss in Target Net: 1.874e-02 |
2020-02-04 05:19:39 Iteration 1050 Training Loss: 1.484e-01 Loss in Target Net: 1.700e-02 |
2020-02-04 05:22:51 Iteration 1100 Training Loss: 1.462e-01 Loss in Target Net: 1.885e-02 |
2020-02-04 05:26:06 Iteration 1150 Training Loss: 1.500e-01 Loss in Target Net: 1.609e-02 |
2020-02-04 05:29:18 Iteration 1200 Training Loss: 1.467e-01 Loss in Target Net: 1.720e-02 |
2020-02-04 05:32:31 Iteration 1250 Training Loss: 1.475e-01 Loss in Target Net: 1.817e-02 |
2020-02-04 05:35:43 Iteration 1300 Training Loss: 1.483e-01 Loss in Target Net: 1.676e-02 |
2020-02-04 05:38:55 Iteration 1350 Training Loss: 1.476e-01 Loss in Target Net: 2.214e-02 |
2020-02-04 05:42:10 Iteration 1400 Training Loss: 1.459e-01 Loss in Target Net: 1.819e-02 |
2020-02-04 05:45:21 Iteration 1450 Training Loss: 1.476e-01 Loss in Target Net: 1.890e-02 |
2020-02-04 05:48:34 Iteration 1499 Training Loss: 1.451e-01 Loss in Target Net: 1.872e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 05:49:26, Epoch 0, Iteration 7, loss 0.629 (0.490), acc 82.692 (90.200) |
2020-02-04 05:54:26, 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:[-3.3911304, -1.1712226, -1.6265923, -1.8235059, -0.82996005, -2.3867986, 8.190894, -2.3221455, 7.100579, -1.4738027], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 05:59:25 Epoch 59, Val iteration 0, acc 94.200 (94.200) |
2020-02-04 06:00:12 Epoch 59, Val iteration 19, acc 94.600 (93.230) |
* Prec: 93.2300018310547 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 97 |
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='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... |
Path: chk-black-end2end/mean-3Repeat/1500/36 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:11:55 Iteration 0 Training Loss: 1.055e+00 Loss in Target Net: 1.375e+00 |
2020-02-04 04:15:04 Iteration 50 Training Loss: 2.368e-01 Loss in Target Net: 8.833e-02 |
2020-02-04 04:18:25 Iteration 100 Training Loss: 2.084e-01 Loss in Target Net: 8.081e-02 |
2020-02-04 04:21:47 Iteration 150 Training Loss: 1.905e-01 Loss in Target Net: 5.813e-02 |
2020-02-04 04:24:59 Iteration 200 Training Loss: 1.838e-01 Loss in Target Net: 5.255e-02 |
2020-02-04 04:27:59 Iteration 250 Training Loss: 1.814e-01 Loss in Target Net: 5.615e-02 |
2020-02-04 04:31:03 Iteration 300 Training Loss: 1.781e-01 Loss in Target Net: 6.836e-02 |
2020-02-04 04:34:09 Iteration 350 Training Loss: 1.734e-01 Loss in Target Net: 7.061e-02 |
2020-02-04 04:37:15 Iteration 400 Training Loss: 1.797e-01 Loss in Target Net: 8.095e-02 |
2020-02-04 04:40:21 Iteration 450 Training Loss: 1.771e-01 Loss in Target Net: 7.526e-02 |
2020-02-04 04:43:26 Iteration 500 Training Loss: 1.710e-01 Loss in Target Net: 5.010e-02 |
2020-02-04 04:46:32 Iteration 550 Training Loss: 1.713e-01 Loss in Target Net: 5.238e-02 |
2020-02-04 04:49:39 Iteration 600 Training Loss: 1.703e-01 Loss in Target Net: 4.429e-02 |
2020-02-04 04:52:45 Iteration 650 Training Loss: 1.692e-01 Loss in Target Net: 4.712e-02 |
2020-02-04 04:55:50 Iteration 700 Training Loss: 1.688e-01 Loss in Target Net: 4.742e-02 |
2020-02-04 04:58:55 Iteration 750 Training Loss: 1.696e-01 Loss in Target Net: 4.804e-02 |
2020-02-04 05:02:02 Iteration 800 Training Loss: 1.682e-01 Loss in Target Net: 4.492e-02 |
2020-02-04 05:05:08 Iteration 850 Training Loss: 1.685e-01 Loss in Target Net: 6.171e-02 |
2020-02-04 05:08:15 Iteration 900 Training Loss: 1.687e-01 Loss in Target Net: 6.155e-02 |
2020-02-04 05:11:20 Iteration 950 Training Loss: 1.673e-01 Loss in Target Net: 7.954e-02 |
2020-02-04 05:14:27 Iteration 1000 Training Loss: 1.678e-01 Loss in Target Net: 5.593e-02 |
2020-02-04 05:17:33 Iteration 1050 Training Loss: 1.624e-01 Loss in Target Net: 5.493e-02 |
2020-02-04 05:20:39 Iteration 1100 Training Loss: 1.633e-01 Loss in Target Net: 6.817e-02 |
2020-02-04 05:23:46 Iteration 1150 Training Loss: 1.716e-01 Loss in Target Net: 4.787e-02 |
2020-02-04 05:26:52 Iteration 1200 Training Loss: 1.658e-01 Loss in Target Net: 5.707e-02 |
2020-02-04 05:29:59 Iteration 1250 Training Loss: 1.657e-01 Loss in Target Net: 6.162e-02 |
2020-02-04 05:33:04 Iteration 1300 Training Loss: 1.641e-01 Loss in Target Net: 5.298e-02 |
2020-02-04 05:36:10 Iteration 1350 Training Loss: 1.666e-01 Loss in Target Net: 5.619e-02 |
2020-02-04 05:39:16 Iteration 1400 Training Loss: 1.694e-01 Loss in Target Net: 4.228e-02 |
2020-02-04 05:42:23 Iteration 1450 Training Loss: 1.690e-01 Loss in Target Net: 8.026e-02 |
2020-02-04 05:45:24 Iteration 1499 Training Loss: 1.718e-01 Loss in Target Net: 5.695e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
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