text stringlengths 5 1.13k |
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2020-01-31 20:14:32 Epoch 59, Val iteration 0, acc 92.000 (92.000) |
2020-01-31 20:14:36 Epoch 59, Val iteration 19, acc 92.600 (92.660) |
* Prec: 92.66000137329101 |
-------- |
GoogLeNet |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:14:39, Epoch 0, Iteration 7, loss 0.103 (0.516), acc 98.077 (89.000) |
2020-01-31 20:14:39, Epoch 30, Iteration 7, loss 0.007 (0.060), acc 100.000 (97.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-19.723612, -5.6676073, -16.243465, -1.0233302, -11.141171, -7.7493773, 8.864588, -14.795216, 11.823522, -26.410414], Poisons' Predictions:[8, 8, 8, 6, 8] |
2020-01-31 20:14:42 Epoch 59, Val iteration 0, acc 91.600 (91.600) |
2020-01-31 20:14:47 Epoch 59, Val iteration 19, acc 92.400 (91.950) |
* Prec: 91.9500015258789 |
-------- |
MobileNetV2 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:14:49, Epoch 0, Iteration 7, loss 1.156 (3.381), acc 78.846 (63.800) |
2020-01-31 20:14:49, Epoch 30, Iteration 7, loss 0.628 (0.478), acc 90.385 (89.400) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-2.2282784, -4.8762107, -10.16408, -8.342666, -32.25797, -14.996453, 24.944563, -36.41437, 26.39485, -31.725332], Poisons' Predictions:[8, 8, 8, 6, 6] |
2020-01-31 20:14:50 Epoch 59, Val iteration 0, acc 88.600 (88.600) |
2020-01-31 20:14:52 Epoch 59, Val iteration 19, acc 88.200 (87.020) |
* Prec: 87.02000160217285 |
-------- |
ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:14:54, Epoch 0, Iteration 7, loss 0.077 (0.995), acc 98.077 (81.400) |
2020-01-31 20:14:54, Epoch 30, Iteration 7, loss 0.006 (0.041), acc 100.000 (98.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-36.67564, -9.537578, -27.152721, 3.0323563, -34.799515, -13.930147, 6.950764, -19.280073, 11.912717, -32.724636], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 20:14:55 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 20:14:57 Epoch 59, Val iteration 19, acc 92.800 (92.470) |
* Prec: 92.47000236511231 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 20:15:00, Epoch 0, Iteration 7, loss 0.465 (0.392), acc 90.385 (93.000) |
2020-01-31 20:15:00, Epoch 30, Iteration 7, loss 0.001 (0.004), acc 100.000 (99.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-6.7578874, -8.414889, -14.099837, -6.025561, -14.618205, -6.669263, 4.496291, -51.053535, 5.551601, -23.4796], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 20:15:02 Epoch 59, Val iteration 0, acc 92.400 (92.400) |
2020-01-31 20:15:06 Epoch 59, Val iteration 19, acc 93.200 (92.890) |
* Prec: 92.8900016784668 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 27 |
TARGET INDEX: 20 |
DPN92 1 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 0 |
GoogLeNet 1 |
MobileNetV2 1 |
ResNet18 1 |
DenseNet121 1 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=4000, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.1, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d', 'GoogLeNet', 'MobileNetV2'], target_index=21, target_label=6, target_net=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d', 'GoogLeNet', 'MobileNetV2', 'ResNet18', 'DenseNet121'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-ourmean/mean/4000/21 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 19:42:52 Iteration 0 Training Loss: 1.115e+00 Loss in Target Net: 4.010e-01 |
2020-01-31 19:43:16 Iteration 50 Training Loss: 8.604e-02 Loss in Target Net: 1.822e-02 |
2020-01-31 19:43:38 Iteration 100 Training Loss: 7.418e-02 Loss in Target Net: 1.781e-02 |
2020-01-31 19:44:01 Iteration 150 Training Loss: 7.097e-02 Loss in Target Net: 1.454e-02 |
2020-01-31 19:44:25 Iteration 200 Training Loss: 7.000e-02 Loss in Target Net: 1.093e-02 |
2020-01-31 19:44:48 Iteration 250 Training Loss: 6.991e-02 Loss in Target Net: 1.545e-02 |
2020-01-31 19:45:10 Iteration 300 Training Loss: 6.765e-02 Loss in Target Net: 1.110e-02 |
2020-01-31 19:45:33 Iteration 350 Training Loss: 7.041e-02 Loss in Target Net: 1.191e-02 |
2020-01-31 19:45:55 Iteration 400 Training Loss: 6.952e-02 Loss in Target Net: 1.057e-02 |
2020-01-31 19:46:17 Iteration 450 Training Loss: 6.398e-02 Loss in Target Net: 1.229e-02 |
2020-01-31 19:46:39 Iteration 500 Training Loss: 6.747e-02 Loss in Target Net: 8.141e-03 |
2020-01-31 19:47:00 Iteration 550 Training Loss: 7.347e-02 Loss in Target Net: 9.561e-03 |
2020-01-31 19:47:22 Iteration 600 Training Loss: 6.402e-02 Loss in Target Net: 1.277e-02 |
2020-01-31 19:47:44 Iteration 650 Training Loss: 6.724e-02 Loss in Target Net: 1.058e-02 |
2020-01-31 19:48:05 Iteration 700 Training Loss: 6.780e-02 Loss in Target Net: 1.280e-02 |
2020-01-31 19:48:27 Iteration 750 Training Loss: 6.676e-02 Loss in Target Net: 1.284e-02 |
2020-01-31 19:48:49 Iteration 800 Training Loss: 6.347e-02 Loss in Target Net: 1.194e-02 |
2020-01-31 19:49:10 Iteration 850 Training Loss: 6.836e-02 Loss in Target Net: 1.043e-02 |
2020-01-31 19:49:32 Iteration 900 Training Loss: 6.544e-02 Loss in Target Net: 9.355e-03 |
2020-01-31 19:49:54 Iteration 950 Training Loss: 6.911e-02 Loss in Target Net: 1.333e-02 |
2020-01-31 19:50:16 Iteration 1000 Training Loss: 6.607e-02 Loss in Target Net: 1.228e-02 |
2020-01-31 19:50:38 Iteration 1050 Training Loss: 6.789e-02 Loss in Target Net: 1.145e-02 |
2020-01-31 19:50:59 Iteration 1100 Training Loss: 5.854e-02 Loss in Target Net: 1.074e-02 |
2020-01-31 19:51:21 Iteration 1150 Training Loss: 7.193e-02 Loss in Target Net: 1.349e-02 |
2020-01-31 19:51:43 Iteration 1200 Training Loss: 6.431e-02 Loss in Target Net: 1.041e-02 |
2020-01-31 19:52:06 Iteration 1250 Training Loss: 6.290e-02 Loss in Target Net: 1.186e-02 |
2020-01-31 19:52:28 Iteration 1300 Training Loss: 6.635e-02 Loss in Target Net: 1.094e-02 |
2020-01-31 19:52:49 Iteration 1350 Training Loss: 6.308e-02 Loss in Target Net: 1.122e-02 |
2020-01-31 19:53:11 Iteration 1400 Training Loss: 6.705e-02 Loss in Target Net: 1.020e-02 |
2020-01-31 19:53:33 Iteration 1450 Training Loss: 6.430e-02 Loss in Target Net: 1.262e-02 |
2020-01-31 19:53:55 Iteration 1500 Training Loss: 6.507e-02 Loss in Target Net: 1.066e-02 |
2020-01-31 19:54:17 Iteration 1550 Training Loss: 6.620e-02 Loss in Target Net: 1.164e-02 |
2020-01-31 19:54:38 Iteration 1600 Training Loss: 6.374e-02 Loss in Target Net: 1.245e-02 |
2020-01-31 19:55:00 Iteration 1650 Training Loss: 6.917e-02 Loss in Target Net: 9.469e-03 |
2020-01-31 19:55:22 Iteration 1700 Training Loss: 6.629e-02 Loss in Target Net: 9.702e-03 |
2020-01-31 19:55:44 Iteration 1750 Training Loss: 6.798e-02 Loss in Target Net: 1.012e-02 |
2020-01-31 19:56:06 Iteration 1800 Training Loss: 6.454e-02 Loss in Target Net: 1.515e-02 |
2020-01-31 19:56:28 Iteration 1850 Training Loss: 6.283e-02 Loss in Target Net: 1.150e-02 |
2020-01-31 19:56:49 Iteration 1900 Training Loss: 6.249e-02 Loss in Target Net: 1.093e-02 |
2020-01-31 19:57:12 Iteration 1950 Training Loss: 6.874e-02 Loss in Target Net: 1.614e-02 |
2020-01-31 19:57:34 Iteration 2000 Training Loss: 6.929e-02 Loss in Target Net: 1.334e-02 |
2020-01-31 19:57:55 Iteration 2050 Training Loss: 6.139e-02 Loss in Target Net: 1.761e-02 |
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