Datasets:
SunayBhat1
/
PureGen_Poisons

Dataset card Data Studio Files
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2020-02-04 21:47:18 Iteration 900 Training Loss: 6.053e-02 Loss in Target Net: 2.027e-03
2020-02-04 21:49:00 Iteration 950 Training Loss: 5.638e-02 Loss in Target Net: 1.440e-03
2020-02-04 21:50:34 Iteration 1000 Training Loss: 6.082e-02 Loss in Target Net: 1.794e-03
2020-02-04 21:52:09 Iteration 1050 Training Loss: 5.987e-02 Loss in Target Net: 2.197e-03
2020-02-04 21:53:42 Iteration 1100 Training Loss: 6.167e-02 Loss in Target Net: 2.267e-03
2020-02-04 21:55:15 Iteration 1150 Training Loss: 5.563e-02 Loss in Target Net: 2.132e-03
2020-02-04 21:56:49 Iteration 1200 Training Loss: 5.703e-02 Loss in Target Net: 2.217e-03
2020-02-04 21:58:19 Iteration 1250 Training Loss: 5.833e-02 Loss in Target Net: 1.821e-03
2020-02-04 21:59:49 Iteration 1300 Training Loss: 5.640e-02 Loss in Target Net: 1.457e-03
2020-02-04 22:01:21 Iteration 1350 Training Loss: 6.042e-02 Loss in Target Net: 2.349e-03
2020-02-04 22:02:56 Iteration 1400 Training Loss: 6.115e-02 Loss in Target Net: 2.208e-03
2020-02-04 22:04:28 Iteration 1450 Training Loss: 5.495e-02 Loss in Target Net: 2.254e-03
2020-02-04 22:06:00 Iteration 1500 Training Loss: 5.499e-02 Loss in Target Net: 1.598e-03
2020-02-04 22:07:31 Iteration 1550 Training Loss: 6.171e-02 Loss in Target Net: 1.877e-03
2020-02-04 22:09:00 Iteration 1600 Training Loss: 6.201e-02 Loss in Target Net: 2.503e-03
2020-02-04 22:10:28 Iteration 1650 Training Loss: 5.602e-02 Loss in Target Net: 1.975e-03
2020-02-04 22:11:52 Iteration 1700 Training Loss: 6.653e-02 Loss in Target Net: 2.007e-03
2020-02-04 22:13:21 Iteration 1750 Training Loss: 5.497e-02 Loss in Target Net: 2.055e-03
2020-02-04 22:14:50 Iteration 1800 Training Loss: 6.190e-02 Loss in Target Net: 2.412e-03
2020-02-04 22:16:27 Iteration 1850 Training Loss: 6.258e-02 Loss in Target Net: 1.808e-03
2020-02-04 22:18:03 Iteration 1900 Training Loss: 5.660e-02 Loss in Target Net: 1.943e-03
2020-02-04 22:19:45 Iteration 1950 Training Loss: 5.348e-02 Loss in Target Net: 1.710e-03
2020-02-04 22:21:28 Iteration 2000 Training Loss: 5.558e-02 Loss in Target Net: 2.371e-03
2020-02-04 22:23:11 Iteration 2050 Training Loss: 6.066e-02 Loss in Target Net: 2.267e-03
2020-02-04 22:24:50 Iteration 2100 Training Loss: 5.716e-02 Loss in Target Net: 2.428e-03
2020-02-04 22:26:28 Iteration 2150 Training Loss: 6.372e-02 Loss in Target Net: 1.815e-03
2020-02-04 22:28:04 Iteration 2200 Training Loss: 6.543e-02 Loss in Target Net: 1.817e-03
2020-02-04 22:29:39 Iteration 2250 Training Loss: 6.103e-02 Loss in Target Net: 2.503e-03
2020-02-04 22:31:14 Iteration 2300 Training Loss: 6.016e-02 Loss in Target Net: 3.493e-03
2020-02-04 22:32:47 Iteration 2350 Training Loss: 5.672e-02 Loss in Target Net: 2.204e-03
2020-02-04 22:34:23 Iteration 2400 Training Loss: 5.764e-02 Loss in Target Net: 1.576e-03
2020-02-04 22:35:59 Iteration 2450 Training Loss: 6.067e-02 Loss in Target Net: 2.275e-03
2020-02-04 22:37:36 Iteration 2500 Training Loss: 6.111e-02 Loss in Target Net: 1.702e-03
2020-02-04 22:39:13 Iteration 2550 Training Loss: 5.516e-02 Loss in Target Net: 1.983e-03
2020-02-04 22:40:50 Iteration 2600 Training Loss: 5.559e-02 Loss in Target Net: 1.794e-03
2020-02-04 22:42:21 Iteration 2650 Training Loss: 5.713e-02 Loss in Target Net: 1.770e-03
2020-02-04 22:43:55 Iteration 2700 Training Loss: 5.482e-02 Loss in Target Net: 1.829e-03
2020-02-04 22:45:25 Iteration 2750 Training Loss: 5.732e-02 Loss in Target Net: 1.833e-03
2020-02-04 22:46:50 Iteration 2800 Training Loss: 6.113e-02 Loss in Target Net: 1.552e-03
2020-02-04 22:48:22 Iteration 2850 Training Loss: 5.896e-02 Loss in Target Net: 1.506e-03
2020-02-04 22:49:57 Iteration 2900 Training Loss: 5.474e-02 Loss in Target Net: 1.281e-03
2020-02-04 22:51:30 Iteration 2950 Training Loss: 5.783e-02 Loss in Target Net: 1.089e-03
2020-02-04 22:53:03 Iteration 3000 Training Loss: 6.224e-02 Loss in Target Net: 1.119e-03
2020-02-04 22:54:39 Iteration 3050 Training Loss: 5.218e-02 Loss in Target Net: 1.179e-03
2020-02-04 22:56:18 Iteration 3100 Training Loss: 5.653e-02 Loss in Target Net: 1.632e-03
2020-02-04 22:57:57 Iteration 3150 Training Loss: 5.869e-02 Loss in Target Net: 1.595e-03
2020-02-04 22:59:34 Iteration 3200 Training Loss: 5.654e-02 Loss in Target Net: 1.122e-03
2020-02-04 23:01:13 Iteration 3250 Training Loss: 5.753e-02 Loss in Target Net: 1.187e-03
2020-02-04 23:02:50 Iteration 3300 Training Loss: 5.808e-02 Loss in Target Net: 1.277e-03
2020-02-04 23:04:25 Iteration 3350 Training Loss: 6.031e-02 Loss in Target Net: 1.882e-03
2020-02-04 23:05:56 Iteration 3400 Training Loss: 5.404e-02 Loss in Target Net: 1.293e-03
2020-02-04 23:07:28 Iteration 3450 Training Loss: 5.617e-02 Loss in Target Net: 1.852e-03
2020-02-04 23:09:00 Iteration 3500 Training Loss: 6.006e-02 Loss in Target Net: 1.896e-03
2020-02-04 23:10:32 Iteration 3550 Training Loss: 5.706e-02 Loss in Target Net: 1.548e-03
2020-02-04 23:12:01 Iteration 3600 Training Loss: 5.879e-02 Loss in Target Net: 1.563e-03
2020-02-04 23:13:27 Iteration 3650 Training Loss: 5.529e-02 Loss in Target Net: 1.719e-03
2020-02-04 23:14:48 Iteration 3700 Training Loss: 5.419e-02 Loss in Target Net: 1.848e-03
2020-02-04 23:16:13 Iteration 3750 Training Loss: 5.919e-02 Loss in Target Net: 1.421e-03
2020-02-04 23:17:34 Iteration 3800 Training Loss: 5.918e-02 Loss in Target Net: 1.305e-03
2020-02-04 23:18:52 Iteration 3850 Training Loss: 6.558e-02 Loss in Target Net: 1.622e-03
2020-02-04 23:20:10 Iteration 3900 Training Loss: 5.661e-02 Loss in Target Net: 1.484e-03
2020-02-04 23:21:24 Iteration 3950 Training Loss: 6.209e-02 Loss in Target Net: 1.506e-03
2020-02-04 23:22:39 Iteration 3999 Training Loss: 6.137e-02 Loss in Target Net: 1.554e-03
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:22:58, Epoch 0, Iteration 7, loss 3.134 (3.657), acc 80.769 (73.800)
2020-02-04 23:22:59, Epoch 30, Iteration 7, loss 0.454 (0.209), acc 96.154 (98.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[9.677767, -63.21172, -86.733955, -6.716352, -37.104202, -9.186226, 36.032314, -61.04367, 35.482246, -99.8005], Poisons' Predictions:[8, 8, 8, 6, 8]
2020-02-04 23:23:27 Epoch 59, Val iteration 0, acc 92.000 (92.000)
2020-02-04 23:24:13 Epoch 59, Val iteration 19, acc 92.200 (92.110)
* Prec: 92.11000175476075
--------
SENet18
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:24:18, Epoch 0, Iteration 7, loss 0.687 (0.859), acc 90.385 (85.600)
2020-02-04 23:24:19, Epoch 30, Iteration 7, loss 0.075 (0.212), acc 98.077 (95.600)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[1.5422744, -2.8795648, -11.400303, -3.7689915, 6.188783, -6.9038167, 20.08692, -12.582866, 18.182657, -7.615837], Poisons' Predictions:[6, 8, 6, 6, 8]
2020-02-04 23:24:23 Epoch 59, Val iteration 0, acc 92.400 (92.400)
2020-02-04 23:24:30 Epoch 59, Val iteration 19, acc 93.400 (91.690)
* Prec: 91.69000129699707
--------
ResNet50
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:24:37, Epoch 0, Iteration 7, loss 0.288 (1.087), acc 94.231 (84.000)
2020-02-04 23:24:38, Epoch 30, Iteration 7, loss 0.000 (0.088), acc 100.000 (99.000)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-16.270182, -46.105885, -37.0931, -99.461815, -31.71565, -29.497707, 31.986387, -1.7369926, 32.577095, -44.957592], Poisons' Predictions:[8, 8, 6, 6, 8]
2020-02-04 23:24:45 Epoch 59, Val iteration 0, acc 93.800 (93.800)
2020-02-04 23:25:05 Epoch 59, Val iteration 19, acc 93.400 (92.620)
* Prec: 92.62000160217285
--------
ResNeXt29_2x64d
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:25:11, Epoch 0, Iteration 7, loss 0.499 (2.360), acc 94.231 (71.000)
2020-02-04 23:25:12, Epoch 30, Iteration 7, loss 0.004 (0.081), acc 100.000 (97.800)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-40.676785, 18.619337, -9.324683, 7.269557, -54.254868, -35.40961, 30.799032, -8.671197, 29.391163, -9.456245], Poisons' Predictions:[8, 8, 8, 6, 8]