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2020-01-31 17:22:00 Iteration 1500 Training Loss: 7.587e-02 Loss in Target Net: 5.652e-03
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2020-01-31 17:22:22 Iteration 1550 Training Loss: 8.047e-02 Loss in Target Net: 1.114e-02
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2020-01-31 17:22:44 Iteration 1600 Training Loss: 7.505e-02 Loss in Target Net: 1.142e-02
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2020-01-31 17:23:06 Iteration 1650 Training Loss: 7.478e-02 Loss in Target Net: 1.274e-02
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2020-01-31 17:23:27 Iteration 1700 Training Loss: 7.406e-02 Loss in Target Net: 9.022e-03
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2020-01-31 17:23:49 Iteration 1750 Training Loss: 7.280e-02 Loss in Target Net: 1.250e-02
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2020-01-31 17:24:10 Iteration 1800 Training Loss: 7.250e-02 Loss in Target Net: 7.969e-03
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2020-01-31 17:24:32 Iteration 1850 Training Loss: 7.051e-02 Loss in Target Net: 1.848e-02
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2020-01-31 17:24:54 Iteration 1900 Training Loss: 7.201e-02 Loss in Target Net: 2.068e-02
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2020-01-31 17:25:16 Iteration 1950 Training Loss: 7.895e-02 Loss in Target Net: 1.558e-02
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2020-01-31 17:25:38 Iteration 2000 Training Loss: 6.959e-02 Loss in Target Net: 2.233e-02
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2020-01-31 17:26:00 Iteration 2050 Training Loss: 7.265e-02 Loss in Target Net: 1.285e-02
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2020-01-31 17:26:21 Iteration 2100 Training Loss: 7.410e-02 Loss in Target Net: 1.313e-02
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2020-01-31 17:26:43 Iteration 2150 Training Loss: 6.723e-02 Loss in Target Net: 1.070e-02
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2020-01-31 17:27:05 Iteration 2200 Training Loss: 7.113e-02 Loss in Target Net: 9.285e-03
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2020-01-31 17:27:27 Iteration 2250 Training Loss: 7.386e-02 Loss in Target Net: 1.086e-02
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2020-01-31 17:27:48 Iteration 2300 Training Loss: 6.994e-02 Loss in Target Net: 9.853e-03
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2020-01-31 17:28:10 Iteration 2350 Training Loss: 7.312e-02 Loss in Target Net: 1.182e-02
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2020-01-31 17:28:32 Iteration 2400 Training Loss: 7.660e-02 Loss in Target Net: 9.871e-03
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2020-01-31 17:28:54 Iteration 2450 Training Loss: 7.385e-02 Loss in Target Net: 7.012e-03
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2020-01-31 17:29:16 Iteration 2500 Training Loss: 6.946e-02 Loss in Target Net: 8.147e-03
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2020-01-31 17:29:37 Iteration 2550 Training Loss: 8.230e-02 Loss in Target Net: 1.007e-02
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2020-01-31 17:29:59 Iteration 2600 Training Loss: 8.086e-02 Loss in Target Net: 6.857e-03
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2020-01-31 17:30:21 Iteration 2650 Training Loss: 7.378e-02 Loss in Target Net: 8.674e-03
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2020-01-31 17:30:43 Iteration 2700 Training Loss: 7.667e-02 Loss in Target Net: 9.677e-03
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2020-01-31 17:31:05 Iteration 2750 Training Loss: 7.704e-02 Loss in Target Net: 1.742e-02
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2020-01-31 17:31:27 Iteration 2800 Training Loss: 7.156e-02 Loss in Target Net: 1.444e-02
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2020-01-31 17:31:49 Iteration 2850 Training Loss: 7.297e-02 Loss in Target Net: 1.664e-02
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2020-01-31 17:32:10 Iteration 2900 Training Loss: 7.910e-02 Loss in Target Net: 1.152e-02
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2020-01-31 17:32:32 Iteration 2950 Training Loss: 7.092e-02 Loss in Target Net: 2.329e-02
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2020-01-31 17:32:54 Iteration 3000 Training Loss: 7.504e-02 Loss in Target Net: 1.418e-02
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2020-01-31 17:33:16 Iteration 3050 Training Loss: 7.762e-02 Loss in Target Net: 1.460e-02
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2020-01-31 17:33:37 Iteration 3100 Training Loss: 6.914e-02 Loss in Target Net: 1.437e-02
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2020-01-31 17:33:59 Iteration 3150 Training Loss: 7.477e-02 Loss in Target Net: 1.165e-02
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2020-01-31 17:34:21 Iteration 3200 Training Loss: 7.445e-02 Loss in Target Net: 1.137e-02
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2020-01-31 17:34:43 Iteration 3250 Training Loss: 7.173e-02 Loss in Target Net: 9.391e-03
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2020-01-31 17:35:05 Iteration 3300 Training Loss: 7.761e-02 Loss in Target Net: 1.239e-02
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2020-01-31 17:35:27 Iteration 3350 Training Loss: 7.034e-02 Loss in Target Net: 1.325e-02
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2020-01-31 17:35:49 Iteration 3400 Training Loss: 7.146e-02 Loss in Target Net: 1.805e-02
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2020-01-31 17:36:10 Iteration 3450 Training Loss: 7.326e-02 Loss in Target Net: 1.136e-02
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2020-01-31 17:36:32 Iteration 3500 Training Loss: 7.234e-02 Loss in Target Net: 8.255e-03
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2020-01-31 17:36:54 Iteration 3550 Training Loss: 7.600e-02 Loss in Target Net: 1.208e-02
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2020-01-31 17:37:16 Iteration 3600 Training Loss: 7.428e-02 Loss in Target Net: 1.163e-02
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2020-01-31 17:37:37 Iteration 3650 Training Loss: 7.874e-02 Loss in Target Net: 1.999e-02
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2020-01-31 17:37:59 Iteration 3700 Training Loss: 7.454e-02 Loss in Target Net: 1.885e-02
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2020-01-31 17:38:21 Iteration 3750 Training Loss: 7.597e-02 Loss in Target Net: 1.256e-02
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2020-01-31 17:38:43 Iteration 3800 Training Loss: 7.216e-02 Loss in Target Net: 1.093e-02
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2020-01-31 17:39:05 Iteration 3850 Training Loss: 7.257e-02 Loss in Target Net: 1.257e-02
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2020-01-31 17:39:27 Iteration 3900 Training Loss: 7.239e-02 Loss in Target Net: 1.380e-02
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2020-01-31 17:39:49 Iteration 3950 Training Loss: 7.044e-02 Loss in Target Net: 1.180e-02
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2020-01-31 17:40:10 Iteration 3999 Training Loss: 7.590e-02 Loss in Target Net: 1.423e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-01-31 17:40:14, Epoch 0, Iteration 7, loss 3.104 (4.401), acc 78.846 (66.800)
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2020-01-31 17:40:15, Epoch 30, Iteration 7, loss 0.005 (0.137), acc 100.000 (98.400)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[31.927309, -3.3395753, -22.407467, 0.08580861, -30.618555, -3.2002702, 33.566345, -49.793514, 39.98758, -60.021988], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-01-31 17:40:19 Epoch 59, Val iteration 0, acc 91.600 (91.600)
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2020-01-31 17:40:26 Epoch 59, Val iteration 19, acc 92.600 (92.420)
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* Prec: 92.42000122070313
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--------
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SENet18
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Using Adam for retraining
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Files already downloaded and verified
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2020-01-31 17:40:28, Epoch 0, Iteration 7, loss 0.636 (0.740), acc 92.308 (87.800)
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2020-01-31 17:40:29, Epoch 30, Iteration 7, loss 0.297 (0.347), acc 94.231 (94.600)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-0.7715794, -34.047024, -20.994556, -10.29683, 1.6083541, -6.3900046, 14.229107, -19.80345, 20.653738, -6.8029246], Poisons' Predictions:[8, 8, 6, 6, 8]
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2020-01-31 17:40:29 Epoch 59, Val iteration 0, acc 91.600 (91.600)
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2020-01-31 17:40:31 Epoch 59, Val iteration 19, acc 92.200 (91.350)
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* Prec: 91.35000152587891
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--------
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ResNet50
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Using Adam for retraining
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Files already downloaded and verified
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2020-01-31 17:40:34, Epoch 0, Iteration 7, loss 0.001 (0.414), acc 100.000 (94.000)
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2020-01-31 17:40:34, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-45.174633, -26.646786, -51.878605, -43.609688, -56.44357, -55.194817, 19.812557, -129.06677, 22.190552, -6.74219], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-01-31 17:40:35 Epoch 59, Val iteration 0, acc 94.200 (94.200)
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2020-01-31 17:40:39 Epoch 59, Val iteration 19, acc 93.400 (93.250)
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* Prec: 93.25000267028808
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--------
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ResNeXt29_2x64d
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Using Adam for retraining
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Files already downloaded and verified
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2020-01-31 17:40:42, Epoch 0, Iteration 7, loss 0.688 (1.612), acc 88.462 (78.600)
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2020-01-31 17:40:42, Epoch 30, Iteration 7, loss 0.046 (0.030), acc 98.077 (99.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-28.374565, 11.170848, 3.6253972, 6.0639997, -57.76487, -37.645466, 13.115352, -15.797018, 35.12304, -22.088818], Poisons' Predictions:[8, 8, 8, 6, 8]
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2020-01-31 17:40:43 Epoch 59, Val iteration 0, acc 91.600 (91.600)
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2020-01-31 17:40:47 Epoch 59, Val iteration 19, acc 91.800 (92.330)
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* Prec: 92.33000221252442
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--------
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GoogLeNet
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Using Adam for retraining
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Files already downloaded and verified
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2020-01-31 17:40:50, Epoch 0, Iteration 7, loss 0.583 (0.514), acc 86.538 (89.000)
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2020-01-31 17:40:50, Epoch 30, Iteration 7, loss 0.022 (0.098), acc 98.077 (97.200)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-20.297995, -0.41000286, -26.969296, -3.5121815, -13.827332, -10.002364, 7.0323424, -18.173557, 7.3535504, -13.036651], Poisons' Predictions:[8, 8, 6, 8, 8]
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2020-01-31 17:40:52 Epoch 59, Val iteration 0, acc 91.200 (91.200)
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2020-01-31 17:40:57 Epoch 59, Val iteration 19, acc 91.000 (91.490)
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