text stringlengths 5 1.13k |
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2020-02-02 11:10:23, Epoch 0, Iteration 7, loss 0.855 (0.508), acc 82.692 (88.800) |
2020-02-02 11:11:22, 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:[-2.6113892, 1.0819747, -4.220945, -1.3872055, -0.92797554, -3.426873, 5.813769, -2.648436, 10.7439165, -2.2859273], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-02 11:12:22 Epoch 59, Val iteration 0, acc 94.200 (94.200) |
2020-02-02 11:12:30 Epoch 59, Val iteration 19, acc 93.800 (93.280) |
* Prec: 93.28000183105469 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 9 |
TARGET INDEX: 4 |
DPN92 1 |
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... |
Path: chk-black-end2end/mean/1500/40 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-02 12:43:58 Iteration 0 Training Loss: 9.941e-01 Loss in Target Net: 1.377e+00 |
2020-02-02 12:44:16 Iteration 50 Training Loss: 2.577e-01 Loss in Target Net: 8.830e-02 |
2020-02-02 12:44:33 Iteration 100 Training Loss: 2.353e-01 Loss in Target Net: 8.759e-02 |
2020-02-02 12:44:51 Iteration 150 Training Loss: 2.138e-01 Loss in Target Net: 7.793e-02 |
2020-02-02 12:45:08 Iteration 200 Training Loss: 2.094e-01 Loss in Target Net: 1.069e-01 |
2020-02-02 12:45:25 Iteration 250 Training Loss: 2.065e-01 Loss in Target Net: 8.009e-02 |
2020-02-02 12:45:41 Iteration 300 Training Loss: 2.082e-01 Loss in Target Net: 7.501e-02 |
2020-02-02 12:45:58 Iteration 350 Training Loss: 1.972e-01 Loss in Target Net: 7.665e-02 |
2020-02-02 12:46:17 Iteration 400 Training Loss: 1.983e-01 Loss in Target Net: 6.369e-02 |
2020-02-02 12:46:34 Iteration 450 Training Loss: 2.024e-01 Loss in Target Net: 5.249e-02 |
2020-02-02 12:46:51 Iteration 500 Training Loss: 1.950e-01 Loss in Target Net: 5.534e-02 |
2020-02-02 12:47:09 Iteration 550 Training Loss: 1.920e-01 Loss in Target Net: 5.131e-02 |
2020-02-02 12:47:26 Iteration 600 Training Loss: 2.031e-01 Loss in Target Net: 4.867e-02 |
2020-02-02 12:47:44 Iteration 650 Training Loss: 1.892e-01 Loss in Target Net: 5.809e-02 |
2020-02-02 12:48:01 Iteration 700 Training Loss: 1.933e-01 Loss in Target Net: 4.409e-02 |
2020-02-02 12:48:19 Iteration 750 Training Loss: 1.915e-01 Loss in Target Net: 5.529e-02 |
2020-02-02 12:48:38 Iteration 800 Training Loss: 1.859e-01 Loss in Target Net: 4.318e-02 |
2020-02-02 12:48:56 Iteration 850 Training Loss: 1.864e-01 Loss in Target Net: 4.282e-02 |
2020-02-02 12:49:13 Iteration 900 Training Loss: 1.896e-01 Loss in Target Net: 4.553e-02 |
2020-02-02 12:49:30 Iteration 950 Training Loss: 1.934e-01 Loss in Target Net: 5.830e-02 |
2020-02-02 12:49:47 Iteration 1000 Training Loss: 1.865e-01 Loss in Target Net: 5.369e-02 |
2020-02-02 12:50:04 Iteration 1050 Training Loss: 1.833e-01 Loss in Target Net: 4.993e-02 |
2020-02-02 12:50:21 Iteration 1100 Training Loss: 1.853e-01 Loss in Target Net: 5.851e-02 |
2020-02-02 12:50:39 Iteration 1150 Training Loss: 1.855e-01 Loss in Target Net: 5.806e-02 |
2020-02-02 12:50:58 Iteration 1200 Training Loss: 1.835e-01 Loss in Target Net: 4.626e-02 |
2020-02-02 12:51:15 Iteration 1250 Training Loss: 1.873e-01 Loss in Target Net: 4.585e-02 |
2020-02-02 12:51:32 Iteration 1300 Training Loss: 1.824e-01 Loss in Target Net: 4.619e-02 |
2020-02-02 12:51:51 Iteration 1350 Training Loss: 1.885e-01 Loss in Target Net: 3.565e-02 |
2020-02-02 12:52:07 Iteration 1400 Training Loss: 1.892e-01 Loss in Target Net: 4.309e-02 |
2020-02-02 12:52:25 Iteration 1450 Training Loss: 1.861e-01 Loss in Target Net: 5.048e-02 |
2020-02-02 12:52:42 Iteration 1499 Training Loss: 1.862e-01 Loss in Target Net: 4.519e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-02 12:52:51, Epoch 0, Iteration 7, loss 0.284 (0.496), acc 86.538 (89.200) |
2020-02-02 12:53:49, 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:[-1.3191082, -1.1050663, 0.17192008, -2.1951604, -2.1467378, -3.5698721, 3.210537, -2.0955296, 10.893505, -1.4458033], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-02 12:54:49 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-02-02 12:54:57 Epoch 59, Val iteration 19, acc 92.200 (93.160) |
* Prec: 93.16000175476074 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 8 |
TARGET INDEX: 40 |
DPN92 1 |
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... |
Path: chk-black-end2end/mean/1500/41 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-02 12:45:12 Iteration 0 Training Loss: 1.029e+00 Loss in Target Net: 1.422e+00 |
2020-02-02 12:45:28 Iteration 50 Training Loss: 2.699e-01 Loss in Target Net: 1.236e-01 |
2020-02-02 12:45:45 Iteration 100 Training Loss: 2.416e-01 Loss in Target Net: 9.964e-02 |
2020-02-02 12:46:01 Iteration 150 Training Loss: 2.315e-01 Loss in Target Net: 8.202e-02 |
2020-02-02 12:46:17 Iteration 200 Training Loss: 2.239e-01 Loss in Target Net: 7.905e-02 |
2020-02-02 12:46:35 Iteration 250 Training Loss: 2.156e-01 Loss in Target Net: 7.581e-02 |
2020-02-02 12:46:53 Iteration 300 Training Loss: 2.102e-01 Loss in Target Net: 6.558e-02 |
2020-02-02 12:47:12 Iteration 350 Training Loss: 2.104e-01 Loss in Target Net: 5.838e-02 |
2020-02-02 12:47:28 Iteration 400 Training Loss: 2.145e-01 Loss in Target Net: 5.475e-02 |
2020-02-02 12:47:47 Iteration 450 Training Loss: 2.157e-01 Loss in Target Net: 5.632e-02 |
2020-02-02 12:48:05 Iteration 500 Training Loss: 2.030e-01 Loss in Target Net: 5.253e-02 |
2020-02-02 12:48:21 Iteration 550 Training Loss: 2.030e-01 Loss in Target Net: 5.611e-02 |
2020-02-02 12:48:39 Iteration 600 Training Loss: 2.032e-01 Loss in Target Net: 5.738e-02 |
2020-02-02 12:48:57 Iteration 650 Training Loss: 2.062e-01 Loss in Target Net: 5.453e-02 |
2020-02-02 12:49:15 Iteration 700 Training Loss: 2.113e-01 Loss in Target Net: 5.042e-02 |
2020-02-02 12:49:31 Iteration 750 Training Loss: 2.019e-01 Loss in Target Net: 4.913e-02 |
2020-02-02 12:49:48 Iteration 800 Training Loss: 2.042e-01 Loss in Target Net: 4.799e-02 |
2020-02-02 12:50:06 Iteration 850 Training Loss: 2.019e-01 Loss in Target Net: 5.115e-02 |
2020-02-02 12:50:23 Iteration 900 Training Loss: 1.986e-01 Loss in Target Net: 5.259e-02 |
2020-02-02 12:50:40 Iteration 950 Training Loss: 1.971e-01 Loss in Target Net: 4.747e-02 |
2020-02-02 12:50:57 Iteration 1000 Training Loss: 1.972e-01 Loss in Target Net: 5.923e-02 |
2020-02-02 12:51:17 Iteration 1050 Training Loss: 2.019e-01 Loss in Target Net: 5.093e-02 |
2020-02-02 12:51:34 Iteration 1100 Training Loss: 1.968e-01 Loss in Target Net: 5.139e-02 |
2020-02-02 12:51:51 Iteration 1150 Training Loss: 1.989e-01 Loss in Target Net: 4.550e-02 |
2020-02-02 12:52:10 Iteration 1200 Training Loss: 2.011e-01 Loss in Target Net: 5.283e-02 |
2020-02-02 12:52:29 Iteration 1250 Training Loss: 1.997e-01 Loss in Target Net: 5.192e-02 |
2020-02-02 12:52:47 Iteration 1300 Training Loss: 1.944e-01 Loss in Target Net: 4.652e-02 |
2020-02-02 12:53:06 Iteration 1350 Training Loss: 2.029e-01 Loss in Target Net: 4.948e-02 |
2020-02-02 12:53:22 Iteration 1400 Training Loss: 1.983e-01 Loss in Target Net: 5.276e-02 |
2020-02-02 12:53:40 Iteration 1450 Training Loss: 1.976e-01 Loss in Target Net: 4.791e-02 |
2020-02-02 12:53:57 Iteration 1499 Training Loss: 1.973e-01 Loss in Target Net: 4.764e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-02 12:54:07, Epoch 0, Iteration 7, loss 0.462 (0.366), acc 90.385 (90.800) |
2020-02-02 12:55:06, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
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