text stringlengths 5 1.13k |
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ResNet18 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:24:50, Epoch 0, Iteration 7, loss 0.499 (0.704), acc 86.538 (86.800) |
2020-02-04 23:24:51, Epoch 30, Iteration 7, loss 0.014 (0.011), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-23.503096, -3.5805123, -15.587045, 3.9058204, -43.590668, -14.652585, 9.716538, -28.445726, 8.817829, -26.793453], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 23:24:52 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-02-04 23:24:58 Epoch 59, Val iteration 19, acc 93.200 (92.750) |
* Prec: 92.75000190734863 |
-------- |
DenseNet121 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 23:25:06, Epoch 0, Iteration 7, loss 0.414 (0.410), acc 92.308 (92.000) |
2020-02-04 23:25:06, Epoch 30, Iteration 7, loss 0.003 (0.003), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-11.724375, -21.492405, -11.544082, -1.71828, -14.8483, -0.80596596, 7.038692, -39.643867, 5.321406, -17.000856], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 23:25:18 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-02-04 23:25:42 Epoch 59, Val iteration 19, acc 92.200 (92.980) |
* Prec: 92.98000144958496 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 119 |
TARGET INDEX: 44 |
DPN92 0 |
SENet18 0 |
ResNet50 0 |
ResNeXt29_2x64d 0 |
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='13', 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=45, 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/4000/45 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 21:21:36 Iteration 0 Training Loss: 1.024e+00 Loss in Target Net: 3.526e-01 |
2020-02-04 21:22:53 Iteration 50 Training Loss: 9.788e-02 Loss in Target Net: 1.269e-02 |
2020-02-04 21:24:11 Iteration 100 Training Loss: 8.038e-02 Loss in Target Net: 1.675e-02 |
2020-02-04 21:25:29 Iteration 150 Training Loss: 8.161e-02 Loss in Target Net: 1.434e-02 |
2020-02-04 21:26:48 Iteration 200 Training Loss: 7.917e-02 Loss in Target Net: 1.134e-02 |
2020-02-04 21:28:08 Iteration 250 Training Loss: 8.058e-02 Loss in Target Net: 1.447e-02 |
2020-02-04 21:29:27 Iteration 300 Training Loss: 7.088e-02 Loss in Target Net: 1.077e-02 |
2020-02-04 21:30:46 Iteration 350 Training Loss: 7.753e-02 Loss in Target Net: 1.127e-02 |
2020-02-04 21:32:05 Iteration 400 Training Loss: 7.788e-02 Loss in Target Net: 1.113e-02 |
2020-02-04 21:33:25 Iteration 450 Training Loss: 7.445e-02 Loss in Target Net: 1.521e-02 |
2020-02-04 21:34:44 Iteration 500 Training Loss: 8.195e-02 Loss in Target Net: 1.297e-02 |
2020-02-04 21:36:04 Iteration 550 Training Loss: 8.097e-02 Loss in Target Net: 1.359e-02 |
2020-02-04 21:37:23 Iteration 600 Training Loss: 7.368e-02 Loss in Target Net: 1.128e-02 |
2020-02-04 21:38:42 Iteration 650 Training Loss: 7.493e-02 Loss in Target Net: 1.317e-02 |
2020-02-04 21:40:04 Iteration 700 Training Loss: 7.065e-02 Loss in Target Net: 1.513e-02 |
2020-02-04 21:41:39 Iteration 750 Training Loss: 7.306e-02 Loss in Target Net: 1.519e-02 |
2020-02-04 21:43:19 Iteration 800 Training Loss: 7.062e-02 Loss in Target Net: 1.300e-02 |
2020-02-04 21:44:59 Iteration 850 Training Loss: 7.325e-02 Loss in Target Net: 1.641e-02 |
2020-02-04 21:46:38 Iteration 900 Training Loss: 7.587e-02 Loss in Target Net: 1.144e-02 |
2020-02-04 21:48:16 Iteration 950 Training Loss: 7.448e-02 Loss in Target Net: 1.236e-02 |
2020-02-04 21:49:51 Iteration 1000 Training Loss: 7.578e-02 Loss in Target Net: 1.333e-02 |
2020-02-04 21:51:22 Iteration 1050 Training Loss: 7.246e-02 Loss in Target Net: 1.471e-02 |
2020-02-04 21:52:54 Iteration 1100 Training Loss: 7.750e-02 Loss in Target Net: 1.541e-02 |
2020-02-04 21:54:26 Iteration 1150 Training Loss: 7.690e-02 Loss in Target Net: 1.390e-02 |
2020-02-04 21:55:57 Iteration 1200 Training Loss: 7.447e-02 Loss in Target Net: 1.576e-02 |
2020-02-04 21:57:28 Iteration 1250 Training Loss: 7.482e-02 Loss in Target Net: 1.728e-02 |
2020-02-04 21:58:57 Iteration 1300 Training Loss: 7.048e-02 Loss in Target Net: 2.149e-02 |
2020-02-04 22:00:25 Iteration 1350 Training Loss: 7.281e-02 Loss in Target Net: 1.626e-02 |
2020-02-04 22:01:53 Iteration 1400 Training Loss: 7.246e-02 Loss in Target Net: 1.700e-02 |
2020-02-04 22:03:22 Iteration 1450 Training Loss: 7.349e-02 Loss in Target Net: 1.486e-02 |
2020-02-04 22:04:52 Iteration 1500 Training Loss: 7.691e-02 Loss in Target Net: 1.404e-02 |
2020-02-04 22:06:23 Iteration 1550 Training Loss: 7.558e-02 Loss in Target Net: 1.493e-02 |
2020-02-04 22:07:50 Iteration 1600 Training Loss: 7.145e-02 Loss in Target Net: 1.524e-02 |
2020-02-04 22:09:22 Iteration 1650 Training Loss: 7.051e-02 Loss in Target Net: 2.144e-02 |
2020-02-04 22:10:46 Iteration 1700 Training Loss: 6.887e-02 Loss in Target Net: 1.459e-02 |
2020-02-04 22:12:13 Iteration 1750 Training Loss: 7.035e-02 Loss in Target Net: 1.631e-02 |
2020-02-04 22:13:37 Iteration 1800 Training Loss: 7.584e-02 Loss in Target Net: 1.588e-02 |
2020-02-04 22:15:04 Iteration 1850 Training Loss: 7.048e-02 Loss in Target Net: 1.729e-02 |
2020-02-04 22:16:36 Iteration 1900 Training Loss: 8.330e-02 Loss in Target Net: 1.359e-02 |
2020-02-04 22:18:09 Iteration 1950 Training Loss: 7.324e-02 Loss in Target Net: 1.345e-02 |
2020-02-04 22:19:48 Iteration 2000 Training Loss: 7.500e-02 Loss in Target Net: 1.669e-02 |
2020-02-04 22:21:27 Iteration 2050 Training Loss: 7.310e-02 Loss in Target Net: 1.554e-02 |
2020-02-04 22:23:06 Iteration 2100 Training Loss: 6.802e-02 Loss in Target Net: 1.793e-02 |
2020-02-04 22:24:45 Iteration 2150 Training Loss: 7.377e-02 Loss in Target Net: 1.620e-02 |
2020-02-04 22:26:20 Iteration 2200 Training Loss: 7.405e-02 Loss in Target Net: 1.606e-02 |
2020-02-04 22:27:54 Iteration 2250 Training Loss: 6.712e-02 Loss in Target Net: 1.640e-02 |
2020-02-04 22:29:26 Iteration 2300 Training Loss: 7.227e-02 Loss in Target Net: 1.689e-02 |
2020-02-04 22:30:58 Iteration 2350 Training Loss: 7.680e-02 Loss in Target Net: 1.464e-02 |
2020-02-04 22:32:29 Iteration 2400 Training Loss: 7.116e-02 Loss in Target Net: 1.325e-02 |
2020-02-04 22:34:04 Iteration 2450 Training Loss: 7.143e-02 Loss in Target Net: 1.599e-02 |
2020-02-04 22:35:39 Iteration 2500 Training Loss: 6.855e-02 Loss in Target Net: 1.482e-02 |
2020-02-04 22:37:13 Iteration 2550 Training Loss: 7.305e-02 Loss in Target Net: 1.511e-02 |
2020-02-04 22:38:50 Iteration 2600 Training Loss: 7.201e-02 Loss in Target Net: 1.694e-02 |
2020-02-04 22:40:25 Iteration 2650 Training Loss: 7.074e-02 Loss in Target Net: 1.345e-02 |
2020-02-04 22:41:55 Iteration 2700 Training Loss: 7.534e-02 Loss in Target Net: 1.494e-02 |
2020-02-04 22:43:28 Iteration 2750 Training Loss: 7.304e-02 Loss in Target Net: 1.353e-02 |
2020-02-04 22:44:58 Iteration 2800 Training Loss: 7.180e-02 Loss in Target Net: 1.555e-02 |
2020-02-04 22:46:23 Iteration 2850 Training Loss: 6.868e-02 Loss in Target Net: 1.162e-02 |
2020-02-04 22:47:48 Iteration 2900 Training Loss: 7.014e-02 Loss in Target Net: 1.205e-02 |
2020-02-04 22:49:20 Iteration 2950 Training Loss: 7.045e-02 Loss in Target Net: 1.296e-02 |
2020-02-04 22:50:52 Iteration 3000 Training Loss: 7.161e-02 Loss in Target Net: 1.295e-02 |
2020-02-04 22:52:24 Iteration 3050 Training Loss: 7.365e-02 Loss in Target Net: 1.271e-02 |
2020-02-04 22:53:56 Iteration 3100 Training Loss: 7.238e-02 Loss in Target Net: 8.988e-03 |
2020-02-04 22:55:34 Iteration 3150 Training Loss: 7.561e-02 Loss in Target Net: 1.433e-02 |
2020-02-04 22:57:12 Iteration 3200 Training Loss: 7.122e-02 Loss in Target Net: 1.597e-02 |
2020-02-04 22:58:50 Iteration 3250 Training Loss: 7.230e-02 Loss in Target Net: 1.929e-02 |
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