text
stringlengths
5
1.13k
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:01, Epoch 0, Iteration 7, loss 4.647 (4.513), acc 80.769 (68.800)
2020-01-31 18:41:01, Epoch 30, Iteration 7, loss 0.275 (0.154), acc 96.154 (97.800)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-8.125312, 4.359811, -60.79544, -5.187841, -46.73677, -17.813938, 32.464817, -47.34054, 25.682161, -95.236084], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 18:41:05 Epoch 59, Val iteration 0, acc 90.400 (90.400)
2020-01-31 18:41:12 Epoch 59, Val iteration 19, acc 91.800 (92.020)
* Prec: 92.02000122070312
--------
SENet18
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:14, Epoch 0, Iteration 7, loss 0.329 (0.781), acc 94.231 (86.800)
2020-01-31 18:41:15, Epoch 30, Iteration 7, loss 0.302 (0.246), acc 96.154 (96.600)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-4.176241, 7.741539, -7.8630047, -2.906169, 4.3067074, -13.891687, 10.90951, -0.15255725, 13.836511, -14.540556], Poisons' Predictions:[6, 8, 8, 8, 6]
2020-01-31 18:41:15 Epoch 59, Val iteration 0, acc 92.600 (92.600)
2020-01-31 18:41:17 Epoch 59, Val iteration 19, acc 93.200 (91.590)
* Prec: 91.59000091552734
--------
ResNet50
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:20, Epoch 0, Iteration 7, loss 0.974 (1.370), acc 98.077 (84.600)
2020-01-31 18:41:20, Epoch 30, Iteration 7, loss 0.017 (0.011), acc 98.077 (99.400)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-52.835827, -83.27959, -68.61045, -65.761955, -71.45612, -18.71898, 35.70137, -33.301544, 37.213074, -42.783607], Poisons' Predictions:[8, 8, 8, 8, 6]
2020-01-31 18:41:21 Epoch 59, Val iteration 0, acc 93.000 (93.000)
2020-01-31 18:41:25 Epoch 59, Val iteration 19, acc 93.600 (93.360)
* Prec: 93.36000137329101
--------
ResNeXt29_2x64d
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:28, Epoch 0, Iteration 7, loss 1.341 (2.375), acc 86.538 (74.600)
2020-01-31 18:41:28, Epoch 30, Iteration 7, loss 0.074 (0.055), acc 96.154 (97.600)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-51.83893, -22.535635, -13.873954, 0.3394588, -67.79066, -28.785084, 22.370821, -47.19964, 20.999834, -35.60719], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 18:41:29 Epoch 59, Val iteration 0, acc 92.600 (92.600)
2020-01-31 18:41:33 Epoch 59, Val iteration 19, acc 92.600 (92.670)
* Prec: 92.67000122070313
--------
GoogLeNet
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:36, Epoch 0, Iteration 7, loss 0.453 (0.359), acc 92.308 (91.200)
2020-01-31 18:41:36, Epoch 30, Iteration 7, loss 0.084 (0.052), acc 98.077 (98.200)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-21.55102, -9.981192, -3.8768363, 0.19463938, -13.008664, 1.1666253, 11.678509, -6.1144423, 8.545883, -19.314234], Poisons' Predictions:[8, 8, 8, 6, 6]
2020-01-31 18:41:39 Epoch 59, Val iteration 0, acc 91.400 (91.400)
2020-01-31 18:41:44 Epoch 59, Val iteration 19, acc 92.000 (91.770)
* Prec: 91.77000160217285
--------
MobileNetV2
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:46, Epoch 0, Iteration 7, loss 1.093 (3.123), acc 86.538 (64.000)
2020-01-31 18:41:46, Epoch 30, Iteration 7, loss 0.030 (0.309), acc 100.000 (93.400)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-7.4072375, -10.812552, -11.209946, 0.8048493, -39.250523, -4.7983885, 1.2187073, -26.421398, 7.1940746, -20.480606], Poisons' Predictions:[8, 6, 8, 8, 6]
2020-01-31 18:41:47 Epoch 59, Val iteration 0, acc 87.800 (87.800)
2020-01-31 18:41:49 Epoch 59, Val iteration 19, acc 87.000 (86.870)
* Prec: 86.87000274658203
--------
ResNet18
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:51, Epoch 0, Iteration 7, loss 0.252 (0.657), acc 92.308 (86.000)
2020-01-31 18:41:51, Epoch 30, Iteration 7, loss 0.098 (0.048), acc 98.077 (98.400)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-25.65529, -4.913412, -8.261774, 1.352952, -40.34906, -7.264841, 10.560951, -22.389574, 10.576748, -30.497385], Poisons' Predictions:[6, 8, 8, 6, 8]
2020-01-31 18:41:51 Epoch 59, Val iteration 0, acc 94.200 (94.200)
2020-01-31 18:41:53 Epoch 59, Val iteration 19, acc 93.600 (92.560)
* Prec: 92.56000213623047
--------
DenseNet121
Using Adam for retraining
Files already downloaded and verified
2020-01-31 18:41:56, Epoch 0, Iteration 7, loss 0.576 (0.399), acc 92.308 (91.600)
2020-01-31 18:41:57, Epoch 30, Iteration 7, loss 0.002 (0.009), acc 100.000 (99.600)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-6.6675587, -12.945102, -10.148052, -2.5767262, -12.00349, -4.7056837, 8.418247, -24.744537, 5.6637, -21.850689], Poisons' Predictions:[8, 8, 8, 8, 6]
2020-01-31 18:41:58 Epoch 59, Val iteration 0, acc 94.000 (94.000)
2020-01-31 18:42:03 Epoch 59, Val iteration 19, acc 93.200 (93.070)
* Prec: 93.07000160217285
--------
------SUMMARY------
TIME ELAPSED (mins): 29
TARGET INDEX: 9
DPN92 0
SENet18 1
ResNet50 1
ResNeXt29_2x64d 0
GoogLeNet 0
MobileNetV2 1
ResNet18 1
DenseNet121 0
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='0', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=0, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean-3Repeat/1500/0
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-04 00:31:54 Iteration 0 Training Loss: 9.933e-01 Loss in Target Net: 1.342e+00
2020-02-04 00:34:58 Iteration 50 Training Loss: 2.494e-01 Loss in Target Net: 9.285e-02
2020-02-04 00:38:08 Iteration 100 Training Loss: 2.236e-01 Loss in Target Net: 7.535e-02
2020-02-04 00:41:18 Iteration 150 Training Loss: 2.086e-01 Loss in Target Net: 6.720e-02
2020-02-04 00:44:29 Iteration 200 Training Loss: 1.991e-01 Loss in Target Net: 6.138e-02
2020-02-04 00:47:40 Iteration 250 Training Loss: 1.960e-01 Loss in Target Net: 4.397e-02