text stringlengths 5 1.13k |
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* Prec: 92.52000198364257 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-01-31 19:11:47, Epoch 0, Iteration 7, loss 0.419 (0.416), acc 90.385 (92.400) |
2020-01-31 19:11:48, Epoch 30, Iteration 7, loss 0.007 (0.005), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:3, Target's Score:[-8.580009, -22.67113, -8.520698, 3.1860628, -25.496836, 2.4586153, -1.4980465, -38.839016, 2.1329992, -11.608425], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-01-31 19:11:50 Epoch 59, Val iteration 0, acc 93.400 (93.400) |
2020-01-31 19:11:54 Epoch 59, Val iteration 19, acc 93.600 (93.120) |
* Prec: 93.12000160217285 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 28 |
TARGET INDEX: 14 |
DPN92 1 |
SENet18 1 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
MobileNetV2 0 |
ResNet18 1 |
DenseNet121 0 |
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=15, 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/4000/15 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:41:24 Iteration 0 Training Loss: 1.113e+00 Loss in Target Net: 5.478e-01 |
2020-01-31 18:41:44 Iteration 50 Training Loss: 1.154e-01 Loss in Target Net: 4.022e-02 |
2020-01-31 18:42:05 Iteration 100 Training Loss: 1.022e-01 Loss in Target Net: 2.784e-02 |
2020-01-31 18:42:27 Iteration 150 Training Loss: 9.931e-02 Loss in Target Net: 2.074e-02 |
2020-01-31 18:42:49 Iteration 200 Training Loss: 9.891e-02 Loss in Target Net: 2.452e-02 |
2020-01-31 18:43:09 Iteration 250 Training Loss: 9.505e-02 Loss in Target Net: 2.799e-02 |
2020-01-31 18:43:31 Iteration 300 Training Loss: 9.440e-02 Loss in Target Net: 2.223e-02 |
2020-01-31 18:43:52 Iteration 350 Training Loss: 9.484e-02 Loss in Target Net: 2.568e-02 |
2020-01-31 18:44:13 Iteration 400 Training Loss: 8.710e-02 Loss in Target Net: 2.492e-02 |
2020-01-31 18:44:33 Iteration 450 Training Loss: 9.875e-02 Loss in Target Net: 2.064e-02 |
2020-01-31 18:44:55 Iteration 500 Training Loss: 9.288e-02 Loss in Target Net: 2.433e-02 |
2020-01-31 18:45:18 Iteration 550 Training Loss: 9.350e-02 Loss in Target Net: 2.550e-02 |
2020-01-31 18:45:39 Iteration 600 Training Loss: 9.002e-02 Loss in Target Net: 2.805e-02 |
2020-01-31 18:46:01 Iteration 650 Training Loss: 9.012e-02 Loss in Target Net: 2.663e-02 |
2020-01-31 18:46:22 Iteration 700 Training Loss: 9.025e-02 Loss in Target Net: 1.621e-02 |
2020-01-31 18:46:43 Iteration 750 Training Loss: 8.860e-02 Loss in Target Net: 1.764e-02 |
2020-01-31 18:47:05 Iteration 800 Training Loss: 8.354e-02 Loss in Target Net: 2.759e-02 |
2020-01-31 18:47:27 Iteration 850 Training Loss: 8.674e-02 Loss in Target Net: 2.243e-02 |
2020-01-31 18:47:50 Iteration 900 Training Loss: 9.025e-02 Loss in Target Net: 2.208e-02 |
2020-01-31 18:48:12 Iteration 950 Training Loss: 8.283e-02 Loss in Target Net: 1.882e-02 |
2020-01-31 18:48:35 Iteration 1000 Training Loss: 8.371e-02 Loss in Target Net: 3.155e-02 |
2020-01-31 18:48:56 Iteration 1050 Training Loss: 8.670e-02 Loss in Target Net: 2.668e-02 |
2020-01-31 18:49:18 Iteration 1100 Training Loss: 9.088e-02 Loss in Target Net: 3.202e-02 |
2020-01-31 18:49:39 Iteration 1150 Training Loss: 8.610e-02 Loss in Target Net: 2.547e-02 |
2020-01-31 18:50:01 Iteration 1200 Training Loss: 8.980e-02 Loss in Target Net: 1.860e-02 |
2020-01-31 18:50:23 Iteration 1250 Training Loss: 8.050e-02 Loss in Target Net: 2.161e-02 |
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2020-01-31 18:56:08 Iteration 2050 Training Loss: 8.320e-02 Loss in Target Net: 1.665e-02 |
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2020-01-31 18:59:12 Iteration 2450 Training Loss: 8.317e-02 Loss in Target Net: 3.095e-02 |
2020-01-31 18:59:35 Iteration 2500 Training Loss: 8.590e-02 Loss in Target Net: 2.989e-02 |
2020-01-31 18:59:56 Iteration 2550 Training Loss: 8.618e-02 Loss in Target Net: 2.341e-02 |
2020-01-31 19:00:18 Iteration 2600 Training Loss: 8.699e-02 Loss in Target Net: 3.087e-02 |
2020-01-31 19:00:39 Iteration 2650 Training Loss: 9.197e-02 Loss in Target Net: 2.809e-02 |
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2020-01-31 19:02:03 Iteration 2850 Training Loss: 8.675e-02 Loss in Target Net: 2.816e-02 |
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2020-01-31 19:03:06 Iteration 3000 Training Loss: 8.624e-02 Loss in Target Net: 2.200e-02 |
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2020-01-31 19:07:23 Iteration 3600 Training Loss: 8.946e-02 Loss in Target Net: 1.943e-02 |
2020-01-31 19:07:46 Iteration 3650 Training Loss: 8.241e-02 Loss in Target Net: 1.900e-02 |
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