text stringlengths 5 1.13k |
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2020-01-31 17:39:55 Epoch 59, Val iteration 0, acc 93.600 (93.600) |
2020-01-31 17:39:59 Epoch 59, Val iteration 19, acc 93.200 (92.900) |
* Prec: 92.90000076293946 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 27 |
TARGET INDEX: 3 |
DPN92 1 |
SENet18 0 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 0 |
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=30, 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/4000/30 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 20:46:14 Iteration 0 Training Loss: 1.100e+00 Loss in Target Net: 3.984e-01 |
2020-01-31 20:46:37 Iteration 50 Training Loss: 1.003e-01 Loss in Target Net: 9.696e-03 |
2020-01-31 20:46:59 Iteration 100 Training Loss: 7.949e-02 Loss in Target Net: 9.856e-03 |
2020-01-31 20:47:24 Iteration 150 Training Loss: 7.055e-02 Loss in Target Net: 1.334e-02 |
2020-01-31 20:47:48 Iteration 200 Training Loss: 7.856e-02 Loss in Target Net: 6.016e-03 |
2020-01-31 20:48:11 Iteration 250 Training Loss: 7.812e-02 Loss in Target Net: 1.499e-02 |
2020-01-31 20:48:33 Iteration 300 Training Loss: 7.033e-02 Loss in Target Net: 1.746e-02 |
2020-01-31 20:48:55 Iteration 350 Training Loss: 7.381e-02 Loss in Target Net: 6.696e-03 |
2020-01-31 20:49:18 Iteration 400 Training Loss: 7.325e-02 Loss in Target Net: 9.203e-03 |
2020-01-31 20:49:40 Iteration 450 Training Loss: 7.040e-02 Loss in Target Net: 1.365e-02 |
2020-01-31 20:50:02 Iteration 500 Training Loss: 7.539e-02 Loss in Target Net: 1.628e-02 |
2020-01-31 20:50:25 Iteration 550 Training Loss: 7.165e-02 Loss in Target Net: 1.115e-02 |
2020-01-31 20:50:48 Iteration 600 Training Loss: 7.129e-02 Loss in Target Net: 1.231e-02 |
2020-01-31 20:51:11 Iteration 650 Training Loss: 7.260e-02 Loss in Target Net: 9.134e-03 |
2020-01-31 20:51:33 Iteration 700 Training Loss: 6.810e-02 Loss in Target Net: 1.390e-02 |
2020-01-31 20:51:56 Iteration 750 Training Loss: 7.088e-02 Loss in Target Net: 8.267e-03 |
2020-01-31 20:52:19 Iteration 800 Training Loss: 6.637e-02 Loss in Target Net: 1.619e-02 |
2020-01-31 20:52:41 Iteration 850 Training Loss: 6.554e-02 Loss in Target Net: 1.371e-02 |
2020-01-31 20:53:05 Iteration 900 Training Loss: 7.073e-02 Loss in Target Net: 2.492e-02 |
2020-01-31 20:53:28 Iteration 950 Training Loss: 7.079e-02 Loss in Target Net: 1.690e-02 |
2020-01-31 20:53:51 Iteration 1000 Training Loss: 6.947e-02 Loss in Target Net: 1.807e-02 |
2020-01-31 20:54:13 Iteration 1050 Training Loss: 7.462e-02 Loss in Target Net: 1.919e-02 |
2020-01-31 20:54:36 Iteration 1100 Training Loss: 6.927e-02 Loss in Target Net: 8.457e-03 |
2020-01-31 20:54:58 Iteration 1150 Training Loss: 7.478e-02 Loss in Target Net: 1.127e-02 |
2020-01-31 20:55:22 Iteration 1200 Training Loss: 7.134e-02 Loss in Target Net: 1.663e-02 |
2020-01-31 20:55:42 Iteration 1250 Training Loss: 7.055e-02 Loss in Target Net: 1.164e-02 |
2020-01-31 20:56:05 Iteration 1300 Training Loss: 6.411e-02 Loss in Target Net: 1.214e-02 |
2020-01-31 20:56:26 Iteration 1350 Training Loss: 7.042e-02 Loss in Target Net: 1.253e-02 |
2020-01-31 20:56:46 Iteration 1400 Training Loss: 6.782e-02 Loss in Target Net: 1.181e-02 |
2020-01-31 20:57:08 Iteration 1450 Training Loss: 7.106e-02 Loss in Target Net: 8.956e-03 |
2020-01-31 20:57:29 Iteration 1500 Training Loss: 6.799e-02 Loss in Target Net: 4.271e-03 |
2020-01-31 20:57:50 Iteration 1550 Training Loss: 7.226e-02 Loss in Target Net: 1.358e-02 |
2020-01-31 20:58:11 Iteration 1600 Training Loss: 6.632e-02 Loss in Target Net: 1.316e-02 |
2020-01-31 20:58:31 Iteration 1650 Training Loss: 6.737e-02 Loss in Target Net: 8.273e-03 |
2020-01-31 20:58:52 Iteration 1700 Training Loss: 6.966e-02 Loss in Target Net: 1.474e-02 |
2020-01-31 20:59:14 Iteration 1750 Training Loss: 6.657e-02 Loss in Target Net: 1.221e-02 |
2020-01-31 20:59:34 Iteration 1800 Training Loss: 7.134e-02 Loss in Target Net: 1.145e-02 |
2020-01-31 20:59:56 Iteration 1850 Training Loss: 6.526e-02 Loss in Target Net: 1.691e-02 |
2020-01-31 21:00:18 Iteration 1900 Training Loss: 6.955e-02 Loss in Target Net: 1.992e-02 |
2020-01-31 21:00:40 Iteration 1950 Training Loss: 7.035e-02 Loss in Target Net: 1.047e-02 |
2020-01-31 21:01:03 Iteration 2000 Training Loss: 7.378e-02 Loss in Target Net: 1.380e-02 |
2020-01-31 21:01:25 Iteration 2050 Training Loss: 7.214e-02 Loss in Target Net: 1.499e-02 |
2020-01-31 21:01:49 Iteration 2100 Training Loss: 6.936e-02 Loss in Target Net: 1.773e-02 |
2020-01-31 21:02:11 Iteration 2150 Training Loss: 6.625e-02 Loss in Target Net: 1.275e-02 |
2020-01-31 21:02:32 Iteration 2200 Training Loss: 6.873e-02 Loss in Target Net: 1.614e-02 |
2020-01-31 21:02:52 Iteration 2250 Training Loss: 7.212e-02 Loss in Target Net: 1.439e-02 |
2020-01-31 21:03:15 Iteration 2300 Training Loss: 6.714e-02 Loss in Target Net: 1.547e-02 |
2020-01-31 21:03:37 Iteration 2350 Training Loss: 6.375e-02 Loss in Target Net: 1.491e-02 |
2020-01-31 21:03:58 Iteration 2400 Training Loss: 6.009e-02 Loss in Target Net: 1.111e-02 |
2020-01-31 21:04:20 Iteration 2450 Training Loss: 6.750e-02 Loss in Target Net: 1.675e-02 |
2020-01-31 21:04:41 Iteration 2500 Training Loss: 6.757e-02 Loss in Target Net: 1.457e-02 |
2020-01-31 21:05:02 Iteration 2550 Training Loss: 6.690e-02 Loss in Target Net: 1.603e-02 |
2020-01-31 21:05:23 Iteration 2600 Training Loss: 6.711e-02 Loss in Target Net: 1.448e-02 |
2020-01-31 21:05:45 Iteration 2650 Training Loss: 6.826e-02 Loss in Target Net: 1.338e-02 |
2020-01-31 21:06:06 Iteration 2700 Training Loss: 6.993e-02 Loss in Target Net: 1.447e-02 |
2020-01-31 21:06:29 Iteration 2750 Training Loss: 6.588e-02 Loss in Target Net: 1.330e-02 |
2020-01-31 21:06:50 Iteration 2800 Training Loss: 7.217e-02 Loss in Target Net: 1.249e-02 |
2020-01-31 21:07:12 Iteration 2850 Training Loss: 6.826e-02 Loss in Target Net: 1.796e-02 |
2020-01-31 21:07:34 Iteration 2900 Training Loss: 6.329e-02 Loss in Target Net: 2.053e-02 |
2020-01-31 21:07:56 Iteration 2950 Training Loss: 6.757e-02 Loss in Target Net: 1.694e-02 |
2020-01-31 21:08:18 Iteration 3000 Training Loss: 7.005e-02 Loss in Target Net: 1.225e-02 |
2020-01-31 21:08:42 Iteration 3050 Training Loss: 6.826e-02 Loss in Target Net: 2.220e-02 |
2020-01-31 21:09:03 Iteration 3100 Training Loss: 6.747e-02 Loss in Target Net: 1.412e-02 |
2020-01-31 21:09:26 Iteration 3150 Training Loss: 6.905e-02 Loss in Target Net: 1.517e-02 |
2020-01-31 21:09:49 Iteration 3200 Training Loss: 6.339e-02 Loss in Target Net: 1.423e-02 |
2020-01-31 21:10:12 Iteration 3250 Training Loss: 6.597e-02 Loss in Target Net: 1.466e-02 |
2020-01-31 21:10:36 Iteration 3300 Training Loss: 7.162e-02 Loss in Target Net: 1.495e-02 |
2020-01-31 21:10:59 Iteration 3350 Training Loss: 6.961e-02 Loss in Target Net: 1.776e-02 |
2020-01-31 21:11:22 Iteration 3400 Training Loss: 6.195e-02 Loss in Target Net: 1.395e-02 |
2020-01-31 21:11:45 Iteration 3450 Training Loss: 6.872e-02 Loss in Target Net: 1.513e-02 |
2020-01-31 21:12:08 Iteration 3500 Training Loss: 6.851e-02 Loss in Target Net: 1.568e-02 |
2020-01-31 21:12:28 Iteration 3550 Training Loss: 6.973e-02 Loss in Target Net: 1.561e-02 |
2020-01-31 21:12:51 Iteration 3600 Training Loss: 6.691e-02 Loss in Target Net: 1.251e-02 |
2020-01-31 21:13:14 Iteration 3650 Training Loss: 7.064e-02 Loss in Target Net: 1.661e-02 |
2020-01-31 21:13:34 Iteration 3700 Training Loss: 6.333e-02 Loss in Target Net: 1.716e-02 |
2020-01-31 21:13:54 Iteration 3750 Training Loss: 6.816e-02 Loss in Target Net: 8.872e-03 |
2020-01-31 21:14:17 Iteration 3800 Training Loss: 6.842e-02 Loss in Target Net: 1.851e-02 |
2020-01-31 21:14:40 Iteration 3850 Training Loss: 6.864e-02 Loss in Target Net: 1.613e-02 |
2020-01-31 21:15:01 Iteration 3900 Training Loss: 7.521e-02 Loss in Target Net: 1.813e-02 |
2020-01-31 21:15:22 Iteration 3950 Training Loss: 6.893e-02 Loss in Target Net: 6.417e-03 |
2020-01-31 21:15:43 Iteration 3999 Training Loss: 7.180e-02 Loss in Target Net: 1.712e-02 |
Evaluating against victims networks |
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