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2020-01-31 18:41:31, Epoch 0, Iteration 7, loss 0.387 (0.468), acc 92.308 (89.600) |
2020-01-31 18:41:31, Epoch 30, Iteration 7, loss 0.004 (0.010), acc 100.000 (99.800) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-8.951261, -22.325026, -20.661732, -6.4695454, -10.217123, -7.588281, 4.085677, -34.041, 5.9661217, -23.544626], Poisons' Predictions:[8, 8, 8, 8, 6] |
2020-01-31 18:41:33 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-01-31 18:41:37 Epoch 59, Val iteration 19, acc 93.200 (92.920) |
* Prec: 92.92000122070313 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 28 |
TARGET INDEX: 10 |
DPN92 0 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 0 |
GoogLeNet 1 |
MobileNetV2 0 |
ResNet18 0 |
DenseNet121 1 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='3', 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=11, 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/4000/11 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:10:44 Iteration 0 Training Loss: 1.116e+00 Loss in Target Net: 4.351e-01 |
2020-01-31 18:11:07 Iteration 50 Training Loss: 1.395e-01 Loss in Target Net: 4.198e-02 |
2020-01-31 18:11:31 Iteration 100 Training Loss: 1.112e-01 Loss in Target Net: 3.663e-02 |
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