Datasets:
SunayBhat1
/
puregen-poisons

Dataset card Data Studio Files Community
1
text
stringlengths
5
1.13k
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 11:24:29 Iteration 0 Training Loss: 1.057e+00 Loss in Target Net: 1.464e+00
2020-02-02 11:24:49 Iteration 50 Training Loss: 2.227e-01 Loss in Target Net: 3.261e-02
2020-02-02 11:25:10 Iteration 100 Training Loss: 1.954e-01 Loss in Target Net: 2.260e-02
2020-02-02 11:25:31 Iteration 150 Training Loss: 1.854e-01 Loss in Target Net: 1.938e-02
2020-02-02 11:25:50 Iteration 200 Training Loss: 1.780e-01 Loss in Target Net: 1.834e-02
2020-02-02 11:26:09 Iteration 250 Training Loss: 1.762e-01 Loss in Target Net: 1.759e-02
2020-02-02 11:26:29 Iteration 300 Training Loss: 1.723e-01 Loss in Target Net: 1.365e-02
2020-02-02 11:26:50 Iteration 350 Training Loss: 1.732e-01 Loss in Target Net: 1.685e-02
2020-02-02 11:27:12 Iteration 400 Training Loss: 1.683e-01 Loss in Target Net: 1.270e-02
2020-02-02 11:27:32 Iteration 450 Training Loss: 1.693e-01 Loss in Target Net: 1.375e-02
2020-02-02 11:27:52 Iteration 500 Training Loss: 1.686e-01 Loss in Target Net: 1.633e-02
2020-02-02 11:28:11 Iteration 550 Training Loss: 1.681e-01 Loss in Target Net: 1.532e-02
2020-02-02 11:28:32 Iteration 600 Training Loss: 1.697e-01 Loss in Target Net: 1.806e-02
2020-02-02 11:28:50 Iteration 650 Training Loss: 1.667e-01 Loss in Target Net: 1.706e-02
2020-02-02 11:29:10 Iteration 700 Training Loss: 1.655e-01 Loss in Target Net: 1.808e-02
2020-02-02 11:29:29 Iteration 750 Training Loss: 1.683e-01 Loss in Target Net: 1.963e-02
2020-02-02 11:29:47 Iteration 800 Training Loss: 1.666e-01 Loss in Target Net: 1.850e-02
2020-02-02 11:30:07 Iteration 850 Training Loss: 1.619e-01 Loss in Target Net: 1.771e-02
2020-02-02 11:30:26 Iteration 900 Training Loss: 1.693e-01 Loss in Target Net: 1.533e-02
2020-02-02 11:30:48 Iteration 950 Training Loss: 1.621e-01 Loss in Target Net: 1.966e-02
2020-02-02 11:31:07 Iteration 1000 Training Loss: 1.627e-01 Loss in Target Net: 2.207e-02
2020-02-02 11:31:24 Iteration 1050 Training Loss: 1.637e-01 Loss in Target Net: 2.201e-02
2020-02-02 11:31:44 Iteration 1100 Training Loss: 1.624e-01 Loss in Target Net: 2.304e-02
2020-02-02 11:32:04 Iteration 1150 Training Loss: 1.626e-01 Loss in Target Net: 1.743e-02
2020-02-02 11:32:25 Iteration 1200 Training Loss: 1.654e-01 Loss in Target Net: 1.700e-02
2020-02-02 11:32:46 Iteration 1250 Training Loss: 1.626e-01 Loss in Target Net: 1.705e-02
2020-02-02 11:33:07 Iteration 1300 Training Loss: 1.622e-01 Loss in Target Net: 1.834e-02
2020-02-02 11:33:27 Iteration 1350 Training Loss: 1.660e-01 Loss in Target Net: 1.435e-02
2020-02-02 11:33:48 Iteration 1400 Training Loss: 1.603e-01 Loss in Target Net: 1.556e-02
2020-02-02 11:34:08 Iteration 1450 Training Loss: 1.660e-01 Loss in Target Net: 1.715e-02
2020-02-02 11:34:26 Iteration 1499 Training Loss: 1.637e-01 Loss in Target Net: 1.400e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-02 11:34:36, Epoch 0, Iteration 7, loss 0.217 (0.388), acc 96.154 (92.400)
2020-02-02 11:35:34, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-0.78024185, -1.851724, -2.5648777, -0.43226755, -1.7898533, -1.7063711, 4.7963724, -2.8898306, 6.725679, 0.9773438], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-02 11:36:34 Epoch 59, Val iteration 0, acc 92.200 (92.200)
2020-02-02 11:36:41 Epoch 59, Val iteration 19, acc 92.400 (92.450)
* Prec: 92.45000114440919
--------
------SUMMARY------
TIME ELAPSED (mins): 10
TARGET INDEX: 13
DPN92 1
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=13, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean/1500/13
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-03 03:59:53 Iteration 0 Training Loss: 1.055e+00 Loss in Target Net: 1.411e+00
2020-02-03 04:00:38 Iteration 50 Training Loss: 2.312e-01 Loss in Target Net: 3.793e-02
2020-02-03 04:01:17 Iteration 100 Training Loss: 2.049e-01 Loss in Target Net: 2.612e-02
2020-02-03 04:02:04 Iteration 150 Training Loss: 1.932e-01 Loss in Target Net: 1.882e-02
2020-02-03 04:02:53 Iteration 200 Training Loss: 1.848e-01 Loss in Target Net: 1.669e-02
2020-02-03 04:03:32 Iteration 250 Training Loss: 1.817e-01 Loss in Target Net: 1.675e-02
2020-02-03 04:04:23 Iteration 300 Training Loss: 1.782e-01 Loss in Target Net: 1.785e-02
2020-02-03 04:05:10 Iteration 350 Training Loss: 1.756e-01 Loss in Target Net: 1.644e-02
2020-02-03 04:05:52 Iteration 400 Training Loss: 1.720e-01 Loss in Target Net: 1.576e-02
2020-02-03 04:06:40 Iteration 450 Training Loss: 1.758e-01 Loss in Target Net: 1.652e-02
2020-02-03 04:07:20 Iteration 500 Training Loss: 1.726e-01 Loss in Target Net: 1.708e-02
2020-02-03 04:08:09 Iteration 550 Training Loss: 1.743e-01 Loss in Target Net: 1.803e-02
2020-02-03 04:08:55 Iteration 600 Training Loss: 1.769e-01 Loss in Target Net: 1.833e-02
2020-02-03 04:09:26 Iteration 650 Training Loss: 1.761e-01 Loss in Target Net: 1.714e-02
2020-02-03 04:10:09 Iteration 700 Training Loss: 1.736e-01 Loss in Target Net: 1.604e-02
2020-02-03 04:10:48 Iteration 750 Training Loss: 1.678e-01 Loss in Target Net: 1.428e-02
2020-02-03 04:11:26 Iteration 800 Training Loss: 1.718e-01 Loss in Target Net: 1.590e-02
2020-02-03 04:12:05 Iteration 850 Training Loss: 1.705e-01 Loss in Target Net: 1.551e-02
2020-02-03 04:12:53 Iteration 900 Training Loss: 1.701e-01 Loss in Target Net: 1.621e-02
2020-02-03 04:13:37 Iteration 950 Training Loss: 1.657e-01 Loss in Target Net: 1.654e-02
2020-02-03 04:14:14 Iteration 1000 Training Loss: 1.711e-01 Loss in Target Net: 1.465e-02
2020-02-03 04:15:01 Iteration 1050 Training Loss: 1.681e-01 Loss in Target Net: 1.934e-02
2020-02-03 04:15:45 Iteration 1100 Training Loss: 1.706e-01 Loss in Target Net: 1.513e-02
2020-02-03 04:16:28 Iteration 1150 Training Loss: 1.698e-01 Loss in Target Net: 1.425e-02
2020-02-03 04:17:14 Iteration 1200 Training Loss: 1.703e-01 Loss in Target Net: 1.505e-02
2020-02-03 04:17:55 Iteration 1250 Training Loss: 1.635e-01 Loss in Target Net: 1.437e-02
2020-02-03 04:18:33 Iteration 1300 Training Loss: 1.680e-01 Loss in Target Net: 1.594e-02
2020-02-03 04:19:21 Iteration 1350 Training Loss: 1.674e-01 Loss in Target Net: 1.355e-02
2020-02-03 04:20:01 Iteration 1400 Training Loss: 1.697e-01 Loss in Target Net: 1.565e-02
2020-02-03 04:20:35 Iteration 1450 Training Loss: 1.665e-01 Loss in Target Net: 1.376e-02
2020-02-03 04:21:22 Iteration 1499 Training Loss: 1.710e-01 Loss in Target Net: 1.257e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-03 04:21:36, Epoch 0, Iteration 7, loss 0.275 (0.467), acc 94.231 (91.600)
2020-02-03 04:22:54, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-3.838046, -1.2580326, 0.0009820154, 0.93262506, -2.3855896, -2.9484186, 8.506102, -1.0347592, 4.820034, -2.3343477], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-03 04:24:36 Epoch 59, Val iteration 0, acc 92.200 (92.200)
2020-02-03 04:24:50 Epoch 59, Val iteration 19, acc 92.000 (92.560)
* Prec: 92.56000137329102
--------
------SUMMARY------
TIME ELAPSED (mins): 21
TARGET INDEX: 13
DPN92 0
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, 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=1500, 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=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, 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'], target_index=14, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean/1500/14
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-02 11:25:13 Iteration 0 Training Loss: 1.080e+00 Loss in Target Net: 1.808e+00