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2020-01-31 21:45:58, Epoch 0, Iteration 7, loss 0.452 (0.490), acc 88.462 (89.000) |
2020-01-31 21:45:58, Epoch 30, Iteration 7, loss 0.005 (0.003), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-6.8213005, -25.072908, -12.335446, -5.1079044, -6.0594907, -11.009349, 9.7083025, -36.844788, 0.4331122, -16.089926], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 21:46:00 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-01-31 21:46:04 Epoch 59, Val iteration 19, acc 92.400 (92.920) |
* Prec: 92.92000122070313 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 30 |
TARGET INDEX: 33 |
DPN92 0 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
MobileNetV2 0 |
ResNet18 0 |
DenseNet121 0 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='2', 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=34, 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/4000/34 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 21:17:06 Iteration 0 Training Loss: 1.151e+00 Loss in Target Net: 4.312e-01 |
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2020-01-31 21:17:52 Iteration 100 Training Loss: 9.003e-02 Loss in Target Net: 1.897e-02 |
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