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2020-01-31 20:43:19 Iteration 300 Training Loss: 7.884e-02 Loss in Target Net: 8.533e-03 |
2020-01-31 20:43:40 Iteration 350 Training Loss: 7.662e-02 Loss in Target Net: 6.355e-03 |
2020-01-31 20:44:03 Iteration 400 Training Loss: 7.452e-02 Loss in Target Net: 6.548e-03 |
2020-01-31 20:44:26 Iteration 450 Training Loss: 7.923e-02 Loss in Target Net: 6.144e-03 |
2020-01-31 20:44:49 Iteration 500 Training Loss: 7.265e-02 Loss in Target Net: 5.656e-03 |
2020-01-31 20:45:12 Iteration 550 Training Loss: 7.107e-02 Loss in Target Net: 6.466e-03 |
2020-01-31 20:45:33 Iteration 600 Training Loss: 7.561e-02 Loss in Target Net: 7.616e-03 |
2020-01-31 20:45:54 Iteration 650 Training Loss: 7.914e-02 Loss in Target Net: 8.305e-03 |
2020-01-31 20:46:16 Iteration 700 Training Loss: 7.759e-02 Loss in Target Net: 7.012e-03 |
2020-01-31 20:46:37 Iteration 750 Training Loss: 7.571e-02 Loss in Target Net: 6.262e-03 |
2020-01-31 20:46:58 Iteration 800 Training Loss: 8.186e-02 Loss in Target Net: 8.776e-03 |
2020-01-31 20:47:18 Iteration 850 Training Loss: 7.264e-02 Loss in Target Net: 7.824e-03 |
2020-01-31 20:47:39 Iteration 900 Training Loss: 7.316e-02 Loss in Target Net: 9.146e-03 |
2020-01-31 20:48:02 Iteration 950 Training Loss: 7.217e-02 Loss in Target Net: 1.285e-02 |
2020-01-31 20:48:23 Iteration 1000 Training Loss: 7.816e-02 Loss in Target Net: 6.704e-03 |
2020-01-31 20:48:45 Iteration 1050 Training Loss: 6.603e-02 Loss in Target Net: 6.530e-03 |
2020-01-31 20:49:07 Iteration 1100 Training Loss: 7.836e-02 Loss in Target Net: 4.343e-03 |
2020-01-31 20:49:29 Iteration 1150 Training Loss: 7.223e-02 Loss in Target Net: 1.018e-02 |
2020-01-31 20:49:51 Iteration 1200 Training Loss: 7.245e-02 Loss in Target Net: 7.297e-03 |
2020-01-31 20:50:14 Iteration 1250 Training Loss: 7.738e-02 Loss in Target Net: 7.538e-03 |
2020-01-31 20:50:35 Iteration 1300 Training Loss: 7.173e-02 Loss in Target Net: 7.408e-03 |
2020-01-31 20:50:56 Iteration 1350 Training Loss: 7.178e-02 Loss in Target Net: 8.532e-03 |
2020-01-31 20:51:17 Iteration 1400 Training Loss: 7.374e-02 Loss in Target Net: 1.073e-02 |
2020-01-31 20:51:38 Iteration 1450 Training Loss: 7.562e-02 Loss in Target Net: 1.055e-02 |
2020-01-31 20:51:59 Iteration 1500 Training Loss: 7.448e-02 Loss in Target Net: 7.210e-03 |
2020-01-31 20:52:21 Iteration 1550 Training Loss: 7.495e-02 Loss in Target Net: 7.642e-03 |
2020-01-31 20:52:42 Iteration 1600 Training Loss: 6.963e-02 Loss in Target Net: 6.049e-03 |
2020-01-31 20:53:04 Iteration 1650 Training Loss: 7.800e-02 Loss in Target Net: 9.415e-03 |
2020-01-31 20:53:26 Iteration 1700 Training Loss: 6.740e-02 Loss in Target Net: 5.054e-03 |
2020-01-31 20:53:48 Iteration 1750 Training Loss: 7.353e-02 Loss in Target Net: 6.431e-03 |
2020-01-31 20:54:10 Iteration 1800 Training Loss: 7.406e-02 Loss in Target Net: 7.314e-03 |
2020-01-31 20:54:33 Iteration 1850 Training Loss: 7.138e-02 Loss in Target Net: 7.856e-03 |
2020-01-31 20:54:56 Iteration 1900 Training Loss: 6.894e-02 Loss in Target Net: 5.293e-03 |
2020-01-31 20:55:17 Iteration 1950 Training Loss: 6.752e-02 Loss in Target Net: 6.363e-03 |
2020-01-31 20:55:42 Iteration 2000 Training Loss: 7.380e-02 Loss in Target Net: 1.308e-02 |
2020-01-31 20:56:05 Iteration 2050 Training Loss: 7.189e-02 Loss in Target Net: 7.642e-03 |
2020-01-31 20:56:28 Iteration 2100 Training Loss: 7.815e-02 Loss in Target Net: 7.108e-03 |
2020-01-31 20:56:52 Iteration 2150 Training Loss: 7.519e-02 Loss in Target Net: 7.980e-03 |
2020-01-31 20:57:14 Iteration 2200 Training Loss: 7.445e-02 Loss in Target Net: 9.021e-03 |
2020-01-31 20:57:35 Iteration 2250 Training Loss: 7.266e-02 Loss in Target Net: 5.133e-03 |
2020-01-31 20:57:58 Iteration 2300 Training Loss: 8.073e-02 Loss in Target Net: 7.892e-03 |
2020-01-31 20:58:19 Iteration 2350 Training Loss: 7.419e-02 Loss in Target Net: 6.202e-03 |
2020-01-31 20:58:42 Iteration 2400 Training Loss: 7.629e-02 Loss in Target Net: 5.848e-03 |
2020-01-31 20:59:05 Iteration 2450 Training Loss: 7.539e-02 Loss in Target Net: 6.695e-03 |
2020-01-31 20:59:27 Iteration 2500 Training Loss: 8.160e-02 Loss in Target Net: 6.663e-03 |
2020-01-31 20:59:49 Iteration 2550 Training Loss: 7.202e-02 Loss in Target Net: 7.981e-03 |
2020-01-31 21:00:12 Iteration 2600 Training Loss: 7.487e-02 Loss in Target Net: 8.067e-03 |
2020-01-31 21:00:34 Iteration 2650 Training Loss: 6.841e-02 Loss in Target Net: 6.232e-03 |
2020-01-31 21:00:56 Iteration 2700 Training Loss: 7.417e-02 Loss in Target Net: 8.188e-03 |
2020-01-31 21:01:19 Iteration 2750 Training Loss: 6.735e-02 Loss in Target Net: 5.997e-03 |
2020-01-31 21:01:41 Iteration 2800 Training Loss: 7.537e-02 Loss in Target Net: 7.542e-03 |
2020-01-31 21:02:03 Iteration 2850 Training Loss: 6.921e-02 Loss in Target Net: 9.446e-03 |
2020-01-31 21:02:25 Iteration 2900 Training Loss: 7.025e-02 Loss in Target Net: 6.996e-03 |
2020-01-31 21:02:47 Iteration 2950 Training Loss: 6.770e-02 Loss in Target Net: 9.756e-03 |
2020-01-31 21:03:08 Iteration 3000 Training Loss: 6.964e-02 Loss in Target Net: 1.127e-02 |
2020-01-31 21:03:29 Iteration 3050 Training Loss: 7.926e-02 Loss in Target Net: 7.641e-03 |
2020-01-31 21:03:51 Iteration 3100 Training Loss: 7.234e-02 Loss in Target Net: 6.600e-03 |
2020-01-31 21:04:14 Iteration 3150 Training Loss: 7.600e-02 Loss in Target Net: 5.914e-03 |
2020-01-31 21:04:35 Iteration 3200 Training Loss: 7.173e-02 Loss in Target Net: 5.722e-03 |
2020-01-31 21:04:55 Iteration 3250 Training Loss: 7.651e-02 Loss in Target Net: 9.374e-03 |
2020-01-31 21:05:18 Iteration 3300 Training Loss: 7.070e-02 Loss in Target Net: 5.840e-03 |
2020-01-31 21:05:40 Iteration 3350 Training Loss: 7.053e-02 Loss in Target Net: 7.917e-03 |
2020-01-31 21:06:03 Iteration 3400 Training Loss: 6.808e-02 Loss in Target Net: 7.687e-03 |
2020-01-31 21:06:24 Iteration 3450 Training Loss: 6.986e-02 Loss in Target Net: 7.429e-03 |
2020-01-31 21:06:44 Iteration 3500 Training Loss: 6.742e-02 Loss in Target Net: 8.714e-03 |
2020-01-31 21:07:05 Iteration 3550 Training Loss: 7.100e-02 Loss in Target Net: 1.101e-02 |
2020-01-31 21:07:27 Iteration 3600 Training Loss: 8.044e-02 Loss in Target Net: 6.509e-03 |
2020-01-31 21:07:49 Iteration 3650 Training Loss: 7.082e-02 Loss in Target Net: 8.732e-03 |
2020-01-31 21:08:10 Iteration 3700 Training Loss: 7.414e-02 Loss in Target Net: 1.054e-02 |
2020-01-31 21:08:30 Iteration 3750 Training Loss: 7.586e-02 Loss in Target Net: 8.931e-03 |
2020-01-31 21:08:52 Iteration 3800 Training Loss: 7.062e-02 Loss in Target Net: 9.087e-03 |
2020-01-31 21:09:13 Iteration 3850 Training Loss: 6.930e-02 Loss in Target Net: 1.007e-02 |
2020-01-31 21:09:34 Iteration 3900 Training Loss: 7.078e-02 Loss in Target Net: 1.039e-02 |
2020-01-31 21:09:55 Iteration 3950 Training Loss: 7.283e-02 Loss in Target Net: 9.633e-03 |
2020-01-31 21:10:15 Iteration 3999 Training Loss: 8.119e-02 Loss in Target Net: 8.630e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:10:20, Epoch 0, Iteration 7, loss 0.820 (3.911), acc 88.462 (73.400) |
2020-01-31 21:10:20, Epoch 30, Iteration 7, loss 0.003 (0.131), acc 100.000 (97.400) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-36.638363, -5.8397274, -49.880386, -6.563649, -26.25135, -7.888669, 20.222612, -78.09784, 27.470798, -116.88213], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 21:10:24 Epoch 59, Val iteration 0, acc 91.400 (91.400) |
2020-01-31 21:10:31 Epoch 59, Val iteration 19, acc 92.800 (92.110) |
* Prec: 92.11000175476075 |
-------- |
SENet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:10:33, Epoch 0, Iteration 7, loss 3.161 (1.062), acc 88.462 (87.400) |
2020-01-31 21:10:33, Epoch 30, Iteration 7, loss 0.269 (0.276), acc 92.308 (94.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-12.220743, -13.828647, -0.35925126, -2.8063297, 4.2262306, -1.804012, 14.825098, -27.034245, 14.203228, -12.232235], Poisons' Predictions:[6, 6, 6, 6, 8] |
2020-01-31 21:10:34 Epoch 59, Val iteration 0, acc 92.400 (92.400) |
2020-01-31 21:10:36 Epoch 59, Val iteration 19, acc 93.400 (91.780) |
* Prec: 91.78000183105469 |
-------- |
ResNet50 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:10:38, Epoch 0, Iteration 7, loss 0.046 (0.920), acc 98.077 (89.000) |
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