text stringlengths 5 1.13k |
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2020-01-31 18:46:25 Epoch 59, Val iteration 0, acc 93.800 (93.800) |
2020-01-31 18:46:29 Epoch 59, Val iteration 19, acc 93.400 (93.110) |
* Prec: 93.11000175476075 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 29 |
TARGET INDEX: 8 |
DPN92 1 |
SENet18 1 |
ResNet50 1 |
ResNeXt29_2x64d 1 |
GoogLeNet 1 |
MobileNetV2 1 |
ResNet18 1 |
DenseNet121 1 |
Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, eval_poison_path='', gpu='1', 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=9, 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/4000/9 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-01-31 18:11:24 Iteration 0 Training Loss: 1.102e+00 Loss in Target Net: 3.672e-01 |
2020-01-31 18:11:47 Iteration 50 Training Loss: 1.196e-01 Loss in Target Net: 4.257e-02 |
2020-01-31 18:12:09 Iteration 100 Training Loss: 9.907e-02 Loss in Target Net: 3.591e-02 |
2020-01-31 18:12:31 Iteration 150 Training Loss: 9.474e-02 Loss in Target Net: 2.659e-02 |
2020-01-31 18:12:53 Iteration 200 Training Loss: 9.179e-02 Loss in Target Net: 2.421e-02 |
2020-01-31 18:13:15 Iteration 250 Training Loss: 9.744e-02 Loss in Target Net: 2.597e-02 |
2020-01-31 18:13:37 Iteration 300 Training Loss: 9.064e-02 Loss in Target Net: 2.767e-02 |
2020-01-31 18:13:59 Iteration 350 Training Loss: 8.343e-02 Loss in Target Net: 2.730e-02 |
2020-01-31 18:14:23 Iteration 400 Training Loss: 8.657e-02 Loss in Target Net: 3.171e-02 |
2020-01-31 18:14:45 Iteration 450 Training Loss: 9.244e-02 Loss in Target Net: 3.114e-02 |
2020-01-31 18:15:07 Iteration 500 Training Loss: 8.368e-02 Loss in Target Net: 2.562e-02 |
2020-01-31 18:15:29 Iteration 550 Training Loss: 9.262e-02 Loss in Target Net: 2.720e-02 |
2020-01-31 18:15:51 Iteration 600 Training Loss: 8.591e-02 Loss in Target Net: 3.233e-02 |
2020-01-31 18:16:14 Iteration 650 Training Loss: 8.952e-02 Loss in Target Net: 2.747e-02 |
2020-01-31 18:16:36 Iteration 700 Training Loss: 8.536e-02 Loss in Target Net: 2.502e-02 |
2020-01-31 18:16:59 Iteration 750 Training Loss: 8.262e-02 Loss in Target Net: 2.535e-02 |
2020-01-31 18:17:21 Iteration 800 Training Loss: 8.334e-02 Loss in Target Net: 3.063e-02 |
2020-01-31 18:17:43 Iteration 850 Training Loss: 8.464e-02 Loss in Target Net: 3.176e-02 |
2020-01-31 18:18:05 Iteration 900 Training Loss: 8.276e-02 Loss in Target Net: 2.251e-02 |
2020-01-31 18:18:27 Iteration 950 Training Loss: 8.071e-02 Loss in Target Net: 2.062e-02 |
2020-01-31 18:18:48 Iteration 1000 Training Loss: 8.920e-02 Loss in Target Net: 1.427e-02 |
2020-01-31 18:19:10 Iteration 1050 Training Loss: 8.511e-02 Loss in Target Net: 2.378e-02 |
2020-01-31 18:19:33 Iteration 1100 Training Loss: 8.618e-02 Loss in Target Net: 2.611e-02 |
2020-01-31 18:19:55 Iteration 1150 Training Loss: 8.872e-02 Loss in Target Net: 1.964e-02 |
2020-01-31 18:20:16 Iteration 1200 Training Loss: 8.439e-02 Loss in Target Net: 1.530e-02 |
2020-01-31 18:20:39 Iteration 1250 Training Loss: 8.138e-02 Loss in Target Net: 1.847e-02 |
2020-01-31 18:21:01 Iteration 1300 Training Loss: 7.994e-02 Loss in Target Net: 1.755e-02 |
2020-01-31 18:21:23 Iteration 1350 Training Loss: 8.994e-02 Loss in Target Net: 3.294e-02 |
2020-01-31 18:21:45 Iteration 1400 Training Loss: 8.130e-02 Loss in Target Net: 2.537e-02 |
2020-01-31 18:22:07 Iteration 1450 Training Loss: 9.203e-02 Loss in Target Net: 2.386e-02 |
2020-01-31 18:22:29 Iteration 1500 Training Loss: 9.002e-02 Loss in Target Net: 2.722e-02 |
2020-01-31 18:22:51 Iteration 1550 Training Loss: 8.532e-02 Loss in Target Net: 2.287e-02 |
2020-01-31 18:23:13 Iteration 1600 Training Loss: 8.498e-02 Loss in Target Net: 1.718e-02 |
2020-01-31 18:23:35 Iteration 1650 Training Loss: 8.695e-02 Loss in Target Net: 2.049e-02 |
2020-01-31 18:23:56 Iteration 1700 Training Loss: 8.182e-02 Loss in Target Net: 2.812e-02 |
2020-01-31 18:24:18 Iteration 1750 Training Loss: 8.452e-02 Loss in Target Net: 1.591e-02 |
2020-01-31 18:24:40 Iteration 1800 Training Loss: 8.666e-02 Loss in Target Net: 1.853e-02 |
2020-01-31 18:25:01 Iteration 1850 Training Loss: 8.516e-02 Loss in Target Net: 1.772e-02 |
2020-01-31 18:25:24 Iteration 1900 Training Loss: 8.119e-02 Loss in Target Net: 1.342e-02 |
2020-01-31 18:25:46 Iteration 1950 Training Loss: 8.389e-02 Loss in Target Net: 1.895e-02 |
2020-01-31 18:26:07 Iteration 2000 Training Loss: 8.309e-02 Loss in Target Net: 1.929e-02 |
2020-01-31 18:26:29 Iteration 2050 Training Loss: 8.382e-02 Loss in Target Net: 1.627e-02 |
2020-01-31 18:26:52 Iteration 2100 Training Loss: 8.335e-02 Loss in Target Net: 1.758e-02 |
2020-01-31 18:27:14 Iteration 2150 Training Loss: 8.491e-02 Loss in Target Net: 1.522e-02 |
2020-01-31 18:27:36 Iteration 2200 Training Loss: 9.719e-02 Loss in Target Net: 1.529e-02 |
2020-01-31 18:27:57 Iteration 2250 Training Loss: 9.231e-02 Loss in Target Net: 2.150e-02 |
2020-01-31 18:28:19 Iteration 2300 Training Loss: 8.870e-02 Loss in Target Net: 2.349e-02 |
2020-01-31 18:28:41 Iteration 2350 Training Loss: 8.030e-02 Loss in Target Net: 1.858e-02 |
2020-01-31 18:29:03 Iteration 2400 Training Loss: 9.154e-02 Loss in Target Net: 2.178e-02 |
2020-01-31 18:29:25 Iteration 2450 Training Loss: 8.208e-02 Loss in Target Net: 2.261e-02 |
2020-01-31 18:29:47 Iteration 2500 Training Loss: 7.660e-02 Loss in Target Net: 2.040e-02 |
2020-01-31 18:30:09 Iteration 2550 Training Loss: 8.875e-02 Loss in Target Net: 2.232e-02 |
2020-01-31 18:30:31 Iteration 2600 Training Loss: 8.741e-02 Loss in Target Net: 2.415e-02 |
2020-01-31 18:30:54 Iteration 2650 Training Loss: 8.688e-02 Loss in Target Net: 1.971e-02 |
2020-01-31 18:31:16 Iteration 2700 Training Loss: 8.542e-02 Loss in Target Net: 2.122e-02 |
2020-01-31 18:31:38 Iteration 2750 Training Loss: 8.934e-02 Loss in Target Net: 1.985e-02 |
2020-01-31 18:32:00 Iteration 2800 Training Loss: 8.325e-02 Loss in Target Net: 2.693e-02 |
2020-01-31 18:32:22 Iteration 2850 Training Loss: 8.748e-02 Loss in Target Net: 2.790e-02 |
2020-01-31 18:32:44 Iteration 2900 Training Loss: 7.945e-02 Loss in Target Net: 2.966e-02 |
2020-01-31 18:33:06 Iteration 2950 Training Loss: 8.870e-02 Loss in Target Net: 2.591e-02 |
2020-01-31 18:33:28 Iteration 3000 Training Loss: 8.168e-02 Loss in Target Net: 2.269e-02 |
2020-01-31 18:33:51 Iteration 3050 Training Loss: 8.198e-02 Loss in Target Net: 2.226e-02 |
2020-01-31 18:34:13 Iteration 3100 Training Loss: 8.884e-02 Loss in Target Net: 2.705e-02 |
2020-01-31 18:34:36 Iteration 3150 Training Loss: 7.754e-02 Loss in Target Net: 2.089e-02 |
2020-01-31 18:34:58 Iteration 3200 Training Loss: 8.108e-02 Loss in Target Net: 1.728e-02 |
2020-01-31 18:35:20 Iteration 3250 Training Loss: 7.834e-02 Loss in Target Net: 2.544e-02 |
2020-01-31 18:35:43 Iteration 3300 Training Loss: 7.376e-02 Loss in Target Net: 2.533e-02 |
2020-01-31 18:36:05 Iteration 3350 Training Loss: 9.087e-02 Loss in Target Net: 2.163e-02 |
2020-01-31 18:36:27 Iteration 3400 Training Loss: 8.738e-02 Loss in Target Net: 2.093e-02 |
2020-01-31 18:36:49 Iteration 3450 Training Loss: 7.879e-02 Loss in Target Net: 2.803e-02 |
2020-01-31 18:37:11 Iteration 3500 Training Loss: 7.455e-02 Loss in Target Net: 3.385e-02 |
2020-01-31 18:37:34 Iteration 3550 Training Loss: 8.955e-02 Loss in Target Net: 2.265e-02 |
2020-01-31 18:37:56 Iteration 3600 Training Loss: 8.524e-02 Loss in Target Net: 2.245e-02 |
2020-01-31 18:38:19 Iteration 3650 Training Loss: 8.084e-02 Loss in Target Net: 2.541e-02 |
2020-01-31 18:38:41 Iteration 3700 Training Loss: 8.148e-02 Loss in Target Net: 1.965e-02 |
2020-01-31 18:39:04 Iteration 3750 Training Loss: 8.291e-02 Loss in Target Net: 2.043e-02 |
2020-01-31 18:39:26 Iteration 3800 Training Loss: 8.341e-02 Loss in Target Net: 1.836e-02 |
2020-01-31 18:39:48 Iteration 3850 Training Loss: 9.143e-02 Loss in Target Net: 1.813e-02 |
2020-01-31 18:40:10 Iteration 3900 Training Loss: 7.623e-02 Loss in Target Net: 1.719e-02 |
2020-01-31 18:40:33 Iteration 3950 Training Loss: 8.171e-02 Loss in Target Net: 3.119e-02 |
2020-01-31 18:40:57 Iteration 3999 Training Loss: 7.959e-02 Loss in Target Net: 2.857e-02 |
Evaluating against victims networks |
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