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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:42:52, Epoch 0, Iteration 7, loss 0.354 (0.571), acc 90.385 (88.000)
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2020-02-02 12:43:51, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-2.1842458, 1.2673564, -1.5319968, -3.3999794, -2.0951254, -2.2412343, 5.4117136, -1.6919502, 10.470913, -3.5539453], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:44:51 Epoch 59, Val iteration 0, acc 92.400 (92.400)
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2020-02-02 12:44:58 Epoch 59, Val iteration 19, acc 93.200 (92.540)
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* Prec: 92.5400016784668
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 37
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DPN92 1
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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=37, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/37
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-03 06:08:50 Iteration 0 Training Loss: 9.764e-01 Loss in Target Net: 1.354e+00
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2020-02-03 06:09:08 Iteration 50 Training Loss: 2.183e-01 Loss in Target Net: 4.035e-02
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2020-02-03 06:09:25 Iteration 100 Training Loss: 1.892e-01 Loss in Target Net: 3.699e-02
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2020-02-03 06:09:41 Iteration 150 Training Loss: 1.798e-01 Loss in Target Net: 3.386e-02
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2020-02-03 06:10:00 Iteration 200 Training Loss: 1.789e-01 Loss in Target Net: 2.909e-02
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2020-02-03 06:10:20 Iteration 250 Training Loss: 1.734e-01 Loss in Target Net: 2.971e-02
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2020-02-03 06:10:36 Iteration 300 Training Loss: 1.693e-01 Loss in Target Net: 3.232e-02
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2020-02-03 06:10:55 Iteration 350 Training Loss: 1.652e-01 Loss in Target Net: 3.213e-02
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2020-02-03 06:11:12 Iteration 400 Training Loss: 1.700e-01 Loss in Target Net: 2.720e-02
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2020-02-03 06:11:32 Iteration 450 Training Loss: 1.642e-01 Loss in Target Net: 2.885e-02
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2020-02-03 06:11:50 Iteration 500 Training Loss: 1.641e-01 Loss in Target Net: 2.241e-02
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2020-02-03 06:12:09 Iteration 550 Training Loss: 1.672e-01 Loss in Target Net: 2.358e-02
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2020-02-03 06:12:28 Iteration 600 Training Loss: 1.592e-01 Loss in Target Net: 2.613e-02
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2020-02-03 06:12:48 Iteration 650 Training Loss: 1.677e-01 Loss in Target Net: 2.890e-02
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2020-02-03 06:13:07 Iteration 700 Training Loss: 1.623e-01 Loss in Target Net: 2.544e-02
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2020-02-03 06:13:26 Iteration 750 Training Loss: 1.669e-01 Loss in Target Net: 2.554e-02
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2020-02-03 06:13:45 Iteration 800 Training Loss: 1.664e-01 Loss in Target Net: 2.419e-02
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2020-02-03 06:14:03 Iteration 850 Training Loss: 1.610e-01 Loss in Target Net: 2.617e-02
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2020-02-03 06:14:21 Iteration 900 Training Loss: 1.617e-01 Loss in Target Net: 2.453e-02
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2020-02-03 06:14:38 Iteration 950 Training Loss: 1.594e-01 Loss in Target Net: 2.306e-02
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2020-02-03 06:14:58 Iteration 1000 Training Loss: 1.598e-01 Loss in Target Net: 2.236e-02
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2020-02-03 06:15:16 Iteration 1050 Training Loss: 1.613e-01 Loss in Target Net: 2.323e-02
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2020-02-03 06:15:35 Iteration 1100 Training Loss: 1.605e-01 Loss in Target Net: 2.571e-02
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2020-02-03 06:15:55 Iteration 1150 Training Loss: 1.591e-01 Loss in Target Net: 2.456e-02
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2020-02-03 06:16:12 Iteration 1200 Training Loss: 1.608e-01 Loss in Target Net: 2.168e-02
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2020-02-03 06:16:30 Iteration 1250 Training Loss: 1.574e-01 Loss in Target Net: 2.747e-02
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2020-02-03 06:16:48 Iteration 1300 Training Loss: 1.571e-01 Loss in Target Net: 2.637e-02
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2020-02-03 06:17:06 Iteration 1350 Training Loss: 1.601e-01 Loss in Target Net: 2.738e-02
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2020-02-03 06:17:25 Iteration 1400 Training Loss: 1.553e-01 Loss in Target Net: 2.510e-02
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2020-02-03 06:17:42 Iteration 1450 Training Loss: 1.573e-01 Loss in Target Net: 2.266e-02
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2020-02-03 06:18:00 Iteration 1499 Training Loss: 1.627e-01 Loss in Target Net: 2.600e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-03 06:18:09, Epoch 0, Iteration 7, loss 0.099 (0.460), acc 98.077 (91.000)
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2020-02-03 06:19:07, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-3.081533, 1.4963449, -2.0458188, -4.6642265, -2.6263428, -0.8154973, 7.1365724, -2.9848034, 11.846841, -3.589666], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 06:20:06 Epoch 59, Val iteration 0, acc 93.600 (93.600)
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2020-02-03 06:20:14 Epoch 59, Val iteration 19, acc 93.800 (93.490)
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* Prec: 93.4900016784668
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 37
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DPN92 1
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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=38, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/38
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:32:34 Iteration 0 Training Loss: 9.669e-01 Loss in Target Net: 1.271e+00
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2020-02-02 12:32:52 Iteration 50 Training Loss: 2.557e-01 Loss in Target Net: 7.831e-02
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2020-02-02 12:33:11 Iteration 100 Training Loss: 2.230e-01 Loss in Target Net: 4.356e-02
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2020-02-02 12:33:30 Iteration 150 Training Loss: 2.129e-01 Loss in Target Net: 4.806e-02
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2020-02-02 12:33:50 Iteration 200 Training Loss: 2.023e-01 Loss in Target Net: 4.134e-02
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2020-02-02 12:34:07 Iteration 250 Training Loss: 1.974e-01 Loss in Target Net: 3.657e-02
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2020-02-02 12:34:25 Iteration 300 Training Loss: 1.968e-01 Loss in Target Net: 3.966e-02
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2020-02-02 12:34:43 Iteration 350 Training Loss: 1.986e-01 Loss in Target Net: 3.547e-02
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2020-02-02 12:35:02 Iteration 400 Training Loss: 1.929e-01 Loss in Target Net: 3.428e-02
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2020-02-02 12:35:20 Iteration 450 Training Loss: 1.970e-01 Loss in Target Net: 2.484e-02
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2020-02-02 12:35:39 Iteration 500 Training Loss: 1.934e-01 Loss in Target Net: 2.679e-02
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2020-02-02 12:35:57 Iteration 550 Training Loss: 1.912e-01 Loss in Target Net: 2.427e-02
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2020-02-02 12:36:17 Iteration 600 Training Loss: 1.884e-01 Loss in Target Net: 2.660e-02
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2020-02-02 12:36:37 Iteration 650 Training Loss: 1.912e-01 Loss in Target Net: 2.967e-02
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2020-02-02 12:36:55 Iteration 700 Training Loss: 1.928e-01 Loss in Target Net: 2.858e-02
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2020-02-02 12:37:14 Iteration 750 Training Loss: 1.885e-01 Loss in Target Net: 2.638e-02
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2020-02-02 12:37:33 Iteration 800 Training Loss: 1.889e-01 Loss in Target Net: 2.898e-02
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2020-02-02 12:37:51 Iteration 850 Training Loss: 1.892e-01 Loss in Target Net: 3.108e-02
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2020-02-02 12:38:09 Iteration 900 Training Loss: 1.864e-01 Loss in Target Net: 2.180e-02
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2020-02-02 12:38:29 Iteration 950 Training Loss: 1.920e-01 Loss in Target Net: 2.432e-02
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2020-02-02 12:38:47 Iteration 1000 Training Loss: 1.850e-01 Loss in Target Net: 2.200e-02
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2020-02-02 12:39:05 Iteration 1050 Training Loss: 1.872e-01 Loss in Target Net: 2.712e-02
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2020-02-02 12:39:24 Iteration 1100 Training Loss: 1.912e-01 Loss in Target Net: 2.278e-02
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2020-02-02 12:39:41 Iteration 1150 Training Loss: 1.867e-01 Loss in Target Net: 2.658e-02
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2020-02-02 12:39:58 Iteration 1200 Training Loss: 1.854e-01 Loss in Target Net: 2.794e-02
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2020-02-02 12:40:18 Iteration 1250 Training Loss: 1.862e-01 Loss in Target Net: 2.627e-02
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2020-02-02 12:40:36 Iteration 1300 Training Loss: 1.791e-01 Loss in Target Net: 2.450e-02
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2020-02-02 12:40:55 Iteration 1350 Training Loss: 1.819e-01 Loss in Target Net: 2.087e-02
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2020-02-02 12:41:13 Iteration 1400 Training Loss: 1.867e-01 Loss in Target Net: 2.076e-02
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2020-02-02 12:41:31 Iteration 1450 Training Loss: 1.874e-01 Loss in Target Net: 2.128e-02
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2020-02-02 12:41:48 Iteration 1499 Training Loss: 1.866e-01 Loss in Target Net: 2.603e-02
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Evaluating against victims networks
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DPN92
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