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2020-02-04 00:46:26 Iteration 200 Training Loss: 1.757e-01 Loss in Target Net: 2.681e-02 |
2020-02-04 00:49:50 Iteration 250 Training Loss: 1.709e-01 Loss in Target Net: 2.502e-02 |
2020-02-04 00:53:10 Iteration 300 Training Loss: 1.667e-01 Loss in Target Net: 2.893e-02 |
2020-02-04 00:56:36 Iteration 350 Training Loss: 1.643e-01 Loss in Target Net: 2.312e-02 |
2020-02-04 01:00:02 Iteration 400 Training Loss: 1.660e-01 Loss in Target Net: 2.680e-02 |
2020-02-04 01:03:26 Iteration 450 Training Loss: 1.613e-01 Loss in Target Net: 3.213e-02 |
2020-02-04 01:06:50 Iteration 500 Training Loss: 1.591e-01 Loss in Target Net: 2.024e-02 |
2020-02-04 01:10:15 Iteration 550 Training Loss: 1.608e-01 Loss in Target Net: 3.560e-02 |
2020-02-04 01:13:39 Iteration 600 Training Loss: 1.565e-01 Loss in Target Net: 2.249e-02 |
2020-02-04 01:17:04 Iteration 650 Training Loss: 1.567e-01 Loss in Target Net: 2.894e-02 |
2020-02-04 01:20:31 Iteration 700 Training Loss: 1.595e-01 Loss in Target Net: 3.084e-02 |
2020-02-04 01:23:58 Iteration 750 Training Loss: 1.549e-01 Loss in Target Net: 2.509e-02 |
2020-02-04 01:27:20 Iteration 800 Training Loss: 1.543e-01 Loss in Target Net: 1.997e-02 |
2020-02-04 01:30:44 Iteration 850 Training Loss: 1.560e-01 Loss in Target Net: 3.500e-02 |
2020-02-04 01:34:06 Iteration 900 Training Loss: 1.537e-01 Loss in Target Net: 3.959e-02 |
2020-02-04 01:37:29 Iteration 950 Training Loss: 1.551e-01 Loss in Target Net: 3.026e-02 |
2020-02-04 01:40:52 Iteration 1000 Training Loss: 1.529e-01 Loss in Target Net: 3.090e-02 |
2020-02-04 01:44:18 Iteration 1050 Training Loss: 1.528e-01 Loss in Target Net: 2.135e-02 |
2020-02-04 01:47:41 Iteration 1100 Training Loss: 1.571e-01 Loss in Target Net: 2.159e-02 |
2020-02-04 01:51:05 Iteration 1150 Training Loss: 1.549e-01 Loss in Target Net: 2.331e-02 |
2020-02-04 01:54:29 Iteration 1200 Training Loss: 1.535e-01 Loss in Target Net: 2.052e-02 |
2020-02-04 01:57:51 Iteration 1250 Training Loss: 1.591e-01 Loss in Target Net: 2.510e-02 |
2020-02-04 02:01:16 Iteration 1300 Training Loss: 1.518e-01 Loss in Target Net: 1.588e-02 |
2020-02-04 02:04:38 Iteration 1350 Training Loss: 1.531e-01 Loss in Target Net: 2.095e-02 |
2020-02-04 02:07:59 Iteration 1400 Training Loss: 1.522e-01 Loss in Target Net: 1.828e-02 |
2020-02-04 02:11:23 Iteration 1450 Training Loss: 1.513e-01 Loss in Target Net: 1.869e-02 |
2020-02-04 02:15:03 Iteration 1499 Training Loss: 1.518e-01 Loss in Target Net: 1.997e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 02:16:09, Epoch 0, Iteration 7, loss 0.580 (0.515), acc 90.385 (90.000) |
2020-02-04 02:21:25, 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:[-2.8342226, -0.53053993, 0.50730246, -1.346465, -0.24233405, -2.3695865, 2.8798046, -2.2273383, 8.618745, -2.1462424], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 02:27:10 Epoch 59, Val iteration 0, acc 93.600 (93.600) |
2020-02-04 02:27:59 Epoch 59, Val iteration 19, acc 93.400 (92.960) |
* Prec: 92.96000099182129 |
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------SUMMARY------ |
TIME ELAPSED (mins): 102 |
TARGET INDEX: 9 |
DPN92 1 |
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