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2020-01-31 21:27:55 Iteration 1500 Training Loss: 7.835e-02 Loss in Target Net: 6.598e-03 |
2020-01-31 21:28:17 Iteration 1550 Training Loss: 7.823e-02 Loss in Target Net: 6.125e-03 |
2020-01-31 21:28:40 Iteration 1600 Training Loss: 7.612e-02 Loss in Target Net: 5.555e-03 |
2020-01-31 21:29:04 Iteration 1650 Training Loss: 7.926e-02 Loss in Target Net: 1.128e-02 |
2020-01-31 21:29:26 Iteration 1700 Training Loss: 8.167e-02 Loss in Target Net: 5.105e-03 |
2020-01-31 21:29:49 Iteration 1750 Training Loss: 7.601e-02 Loss in Target Net: 1.036e-02 |
2020-01-31 21:30:11 Iteration 1800 Training Loss: 7.310e-02 Loss in Target Net: 8.220e-03 |
2020-01-31 21:30:34 Iteration 1850 Training Loss: 7.276e-02 Loss in Target Net: 5.339e-03 |
2020-01-31 21:30:56 Iteration 1900 Training Loss: 7.192e-02 Loss in Target Net: 4.763e-03 |
2020-01-31 21:31:19 Iteration 1950 Training Loss: 7.650e-02 Loss in Target Net: 5.939e-03 |
2020-01-31 21:31:40 Iteration 2000 Training Loss: 7.372e-02 Loss in Target Net: 8.749e-03 |
2020-01-31 21:32:02 Iteration 2050 Training Loss: 7.496e-02 Loss in Target Net: 8.823e-03 |
2020-01-31 21:32:25 Iteration 2100 Training Loss: 7.639e-02 Loss in Target Net: 4.023e-03 |
2020-01-31 21:32:47 Iteration 2150 Training Loss: 7.666e-02 Loss in Target Net: 7.433e-03 |
2020-01-31 21:33:08 Iteration 2200 Training Loss: 8.992e-02 Loss in Target Net: 4.491e-03 |
2020-01-31 21:33:30 Iteration 2250 Training Loss: 8.642e-02 Loss in Target Net: 7.599e-03 |
2020-01-31 21:33:52 Iteration 2300 Training Loss: 7.788e-02 Loss in Target Net: 5.948e-03 |
2020-01-31 21:34:14 Iteration 2350 Training Loss: 7.776e-02 Loss in Target Net: 7.431e-03 |
2020-01-31 21:34:36 Iteration 2400 Training Loss: 7.303e-02 Loss in Target Net: 4.605e-03 |
2020-01-31 21:34:58 Iteration 2450 Training Loss: 7.824e-02 Loss in Target Net: 8.360e-03 |
2020-01-31 21:35:20 Iteration 2500 Training Loss: 7.787e-02 Loss in Target Net: 6.357e-03 |
2020-01-31 21:35:42 Iteration 2550 Training Loss: 8.112e-02 Loss in Target Net: 5.852e-03 |
2020-01-31 21:36:04 Iteration 2600 Training Loss: 8.716e-02 Loss in Target Net: 5.844e-03 |
2020-01-31 21:36:26 Iteration 2650 Training Loss: 7.433e-02 Loss in Target Net: 6.905e-03 |
2020-01-31 21:36:48 Iteration 2700 Training Loss: 8.084e-02 Loss in Target Net: 7.467e-03 |
2020-01-31 21:37:10 Iteration 2750 Training Loss: 8.577e-02 Loss in Target Net: 1.222e-02 |
2020-01-31 21:37:31 Iteration 2800 Training Loss: 8.166e-02 Loss in Target Net: 5.598e-03 |
2020-01-31 21:37:53 Iteration 2850 Training Loss: 7.534e-02 Loss in Target Net: 6.428e-03 |
2020-01-31 21:38:14 Iteration 2900 Training Loss: 7.804e-02 Loss in Target Net: 8.288e-03 |
2020-01-31 21:38:36 Iteration 2950 Training Loss: 7.420e-02 Loss in Target Net: 6.426e-03 |
2020-01-31 21:38:58 Iteration 3000 Training Loss: 8.053e-02 Loss in Target Net: 6.200e-03 |
2020-01-31 21:39:19 Iteration 3050 Training Loss: 8.172e-02 Loss in Target Net: 9.377e-03 |
2020-01-31 21:39:41 Iteration 3100 Training Loss: 7.978e-02 Loss in Target Net: 7.279e-03 |
2020-01-31 21:40:03 Iteration 3150 Training Loss: 7.540e-02 Loss in Target Net: 8.138e-03 |
2020-01-31 21:40:24 Iteration 3200 Training Loss: 8.548e-02 Loss in Target Net: 7.475e-03 |
2020-01-31 21:40:46 Iteration 3250 Training Loss: 7.327e-02 Loss in Target Net: 6.917e-03 |
2020-01-31 21:41:08 Iteration 3300 Training Loss: 8.255e-02 Loss in Target Net: 6.729e-03 |
2020-01-31 21:41:30 Iteration 3350 Training Loss: 8.101e-02 Loss in Target Net: 9.268e-03 |
2020-01-31 21:41:51 Iteration 3400 Training Loss: 7.680e-02 Loss in Target Net: 7.721e-03 |
2020-01-31 21:42:14 Iteration 3450 Training Loss: 8.390e-02 Loss in Target Net: 6.337e-03 |
2020-01-31 21:42:35 Iteration 3500 Training Loss: 7.510e-02 Loss in Target Net: 7.666e-03 |
2020-01-31 21:42:57 Iteration 3550 Training Loss: 7.425e-02 Loss in Target Net: 9.127e-03 |
2020-01-31 21:43:19 Iteration 3600 Training Loss: 7.319e-02 Loss in Target Net: 6.232e-03 |
2020-01-31 21:43:40 Iteration 3650 Training Loss: 8.219e-02 Loss in Target Net: 6.121e-03 |
2020-01-31 21:44:02 Iteration 3700 Training Loss: 7.745e-02 Loss in Target Net: 5.379e-03 |
2020-01-31 21:44:24 Iteration 3750 Training Loss: 7.691e-02 Loss in Target Net: 4.934e-03 |
2020-01-31 21:44:45 Iteration 3800 Training Loss: 7.205e-02 Loss in Target Net: 4.190e-03 |
2020-01-31 21:45:06 Iteration 3850 Training Loss: 7.835e-02 Loss in Target Net: 1.063e-02 |
2020-01-31 21:45:28 Iteration 3900 Training Loss: 7.567e-02 Loss in Target Net: 6.632e-03 |
2020-01-31 21:45:50 Iteration 3950 Training Loss: 7.562e-02 Loss in Target Net: 6.949e-03 |
2020-01-31 21:46:12 Iteration 3999 Training Loss: 7.796e-02 Loss in Target Net: 1.152e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:46:17, Epoch 0, Iteration 7, loss 1.249 (2.769), acc 94.231 (75.600) |
2020-01-31 21:46:17, Epoch 30, Iteration 7, loss 0.129 (0.143), acc 96.154 (97.600) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[1.5929216, -25.142237, -40.53896, 10.78334, -39.286007, 4.180576, 32.647263, -59.74452, 29.322073, -91.52158], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:46:22 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-01-31 21:46:29 Epoch 59, Val iteration 19, acc 92.400 (92.300) |
* Prec: 92.30000152587891 |
-------- |
SENet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:46:31, Epoch 0, Iteration 7, loss 0.807 (0.700), acc 84.615 (88.000) |
2020-01-31 21:46:32, Epoch 30, Iteration 7, loss 0.038 (0.163), acc 98.077 (97.800) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-6.348503, -8.863961, -8.859324, 0.17112923, 12.270578, -3.3942864, 33.73919, -17.090223, 18.36741, -13.241372], Poisons' Predictions:[8, 6, 6, 8, 8] |
2020-01-31 21:46:33 Epoch 59, Val iteration 0, acc 91.600 (91.600) |
2020-01-31 21:46:34 Epoch 59, Val iteration 19, acc 91.000 (90.970) |
* Prec: 90.97000160217286 |
-------- |
ResNet50 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:46:37, Epoch 0, Iteration 7, loss 0.947 (1.133), acc 98.077 (88.200) |
2020-01-31 21:46:37, Epoch 30, Iteration 7, loss 0.000 (0.028), acc 100.000 (99.600) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-75.1662, -88.3623, -69.13102, -28.781052, -43.293423, -68.004425, 0.6297326, -94.716064, 6.3997526, -51.53053], Poisons' Predictions:[8, 8, 6, 8, 8] |
2020-01-31 21:46:38 Epoch 59, Val iteration 0, acc 93.200 (93.200) |
2020-01-31 21:46:42 Epoch 59, Val iteration 19, acc 94.000 (93.680) |
* Prec: 93.68000144958496 |
-------- |
ResNeXt29_2x64d |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:46:45, Epoch 0, Iteration 7, loss 0.474 (2.076), acc 92.308 (75.400) |
2020-01-31 21:46:45, Epoch 30, Iteration 7, loss 0.159 (0.041), acc 92.308 (98.400) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-42.092632, -37.06679, -13.60797, 1.9458525, -62.509853, -16.17919, 20.605165, -38.51055, 17.943872, -18.257492], Poisons' Predictions:[8, 6, 8, 8, 8] |
2020-01-31 21:46:46 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 21:46:50 Epoch 59, Val iteration 19, acc 93.200 (93.030) |
* Prec: 93.03000144958496 |
-------- |
GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 21:46:53, Epoch 0, Iteration 7, loss 0.595 (0.433), acc 88.462 (89.800) |
2020-01-31 21:46:53, Epoch 30, Iteration 7, loss 0.048 (0.051), acc 98.077 (97.800) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-19.659979, -17.021742, -7.322701, -1.0833173, -10.120907, 0.6178453, 8.562868, -9.911739, 5.0030365, -19.176136], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:46:56 Epoch 59, Val iteration 0, acc 91.800 (91.800) |
2020-01-31 21:47:00 Epoch 59, Val iteration 19, acc 92.000 (92.430) |
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