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2020-02-02 11:48:25 Iteration 650 Training Loss: 1.591e-01 Loss in Target Net: 2.082e-02
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2020-02-02 11:48:42 Iteration 700 Training Loss: 1.625e-01 Loss in Target Net: 2.203e-02
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2020-02-02 11:49:01 Iteration 750 Training Loss: 1.651e-01 Loss in Target Net: 2.335e-02
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2020-02-02 11:49:20 Iteration 800 Training Loss: 1.616e-01 Loss in Target Net: 2.138e-02
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2020-02-02 11:49:38 Iteration 850 Training Loss: 1.634e-01 Loss in Target Net: 1.923e-02
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2020-02-02 11:49:55 Iteration 900 Training Loss: 1.613e-01 Loss in Target Net: 2.130e-02
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2020-02-02 11:50:13 Iteration 950 Training Loss: 1.562e-01 Loss in Target Net: 2.049e-02
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2020-02-02 11:50:30 Iteration 1000 Training Loss: 1.573e-01 Loss in Target Net: 2.152e-02
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2020-02-02 11:50:46 Iteration 1050 Training Loss: 1.600e-01 Loss in Target Net: 1.981e-02
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2020-02-02 11:51:02 Iteration 1100 Training Loss: 1.595e-01 Loss in Target Net: 2.455e-02
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2020-02-02 11:51:18 Iteration 1150 Training Loss: 1.559e-01 Loss in Target Net: 2.350e-02
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2020-02-02 11:51:34 Iteration 1200 Training Loss: 1.576e-01 Loss in Target Net: 2.649e-02
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2020-02-02 11:51:51 Iteration 1250 Training Loss: 1.562e-01 Loss in Target Net: 2.338e-02
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2020-02-02 11:52:08 Iteration 1300 Training Loss: 1.590e-01 Loss in Target Net: 2.492e-02
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2020-02-02 11:52:25 Iteration 1350 Training Loss: 1.591e-01 Loss in Target Net: 2.141e-02
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2020-02-02 11:52:42 Iteration 1400 Training Loss: 1.597e-01 Loss in Target Net: 2.140e-02
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2020-02-02 11:52:58 Iteration 1450 Training Loss: 1.582e-01 Loss in Target Net: 2.397e-02
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2020-02-02 11:53:15 Iteration 1499 Training Loss: 1.583e-01 Loss in Target Net: 2.579e-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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2020-02-02 11:53:24, Epoch 0, Iteration 7, loss 0.434 (0.513), acc 86.538 (89.200)
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2020-02-02 11:54:21, 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:6, Target's Score:[-1.8448409, -0.4362211, -0.51770365, 0.062275317, -1.0117327, -2.4048147, 7.5949187, -3.7670476, 6.81492, -4.347653], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:55:21 Epoch 59, Val iteration 0, acc 92.800 (92.800)
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2020-02-02 11:55:28 Epoch 59, Val iteration 19, acc 93.000 (93.200)
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* Prec: 93.2000015258789
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 23
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DPN92 0
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='0', 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=24, 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/1500/24
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:57:43 Iteration 0 Training Loss: 1.053e+00 Loss in Target Net: 1.441e+00
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2020-02-02 11:58:02 Iteration 50 Training Loss: 2.407e-01 Loss in Target Net: 5.691e-02
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2020-02-02 11:58:19 Iteration 100 Training Loss: 2.102e-01 Loss in Target Net: 4.535e-02
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2020-02-02 11:58:36 Iteration 150 Training Loss: 1.939e-01 Loss in Target Net: 4.206e-02
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2020-02-02 11:58:55 Iteration 200 Training Loss: 1.859e-01 Loss in Target Net: 4.576e-02
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2020-02-02 11:59:14 Iteration 250 Training Loss: 1.844e-01 Loss in Target Net: 3.794e-02
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2020-02-02 11:59:31 Iteration 300 Training Loss: 1.769e-01 Loss in Target Net: 3.725e-02
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2020-02-02 11:59:50 Iteration 350 Training Loss: 1.751e-01 Loss in Target Net: 3.713e-02
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2020-02-02 12:00:09 Iteration 400 Training Loss: 1.703e-01 Loss in Target Net: 3.602e-02
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2020-02-02 12:00:29 Iteration 450 Training Loss: 1.757e-01 Loss in Target Net: 3.354e-02
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2020-02-02 12:00:48 Iteration 500 Training Loss: 1.690e-01 Loss in Target Net: 3.412e-02
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2020-02-02 12:01:07 Iteration 550 Training Loss: 1.688e-01 Loss in Target Net: 3.226e-02
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2020-02-02 12:01:25 Iteration 600 Training Loss: 1.684e-01 Loss in Target Net: 3.219e-02
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2020-02-02 12:01:41 Iteration 650 Training Loss: 1.666e-01 Loss in Target Net: 3.169e-02
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2020-02-02 12:02:00 Iteration 700 Training Loss: 1.666e-01 Loss in Target Net: 2.994e-02
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2020-02-02 12:02:17 Iteration 750 Training Loss: 1.661e-01 Loss in Target Net: 2.915e-02
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2020-02-02 12:02:34 Iteration 800 Training Loss: 1.691e-01 Loss in Target Net: 2.599e-02
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2020-02-02 12:02:52 Iteration 850 Training Loss: 1.631e-01 Loss in Target Net: 2.950e-02
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2020-02-02 12:03:12 Iteration 900 Training Loss: 1.661e-01 Loss in Target Net: 3.203e-02
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2020-02-02 12:03:31 Iteration 950 Training Loss: 1.674e-01 Loss in Target Net: 2.688e-02
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2020-02-02 12:03:50 Iteration 1000 Training Loss: 1.609e-01 Loss in Target Net: 2.736e-02
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2020-02-02 12:04:08 Iteration 1050 Training Loss: 1.655e-01 Loss in Target Net: 2.842e-02
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2020-02-02 12:04:27 Iteration 1100 Training Loss: 1.674e-01 Loss in Target Net: 2.469e-02
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2020-02-02 12:04:47 Iteration 1150 Training Loss: 1.705e-01 Loss in Target Net: 2.544e-02
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2020-02-02 12:05:06 Iteration 1200 Training Loss: 1.642e-01 Loss in Target Net: 2.621e-02
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2020-02-02 12:05:25 Iteration 1250 Training Loss: 1.652e-01 Loss in Target Net: 2.894e-02
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2020-02-02 12:05:46 Iteration 1300 Training Loss: 1.677e-01 Loss in Target Net: 2.877e-02
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2020-02-02 12:06:07 Iteration 1350 Training Loss: 1.651e-01 Loss in Target Net: 2.742e-02
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2020-02-02 12:06:27 Iteration 1400 Training Loss: 1.639e-01 Loss in Target Net: 2.819e-02
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2020-02-02 12:06:46 Iteration 1450 Training Loss: 1.634e-01 Loss in Target Net: 3.080e-02
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2020-02-02 12:07:06 Iteration 1499 Training Loss: 1.650e-01 Loss in Target Net: 3.260e-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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2020-02-02 12:07:16, Epoch 0, Iteration 7, loss 0.119 (0.358), acc 98.077 (93.000)
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2020-02-02 12:08:14, 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:6, Target's Score:[-3.5945213, -0.2844378, -1.8448142, -0.82890344, 0.6380246, -2.6401217, 7.74634, -3.138221, 6.882024, -2.588488], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:09:14 Epoch 59, Val iteration 0, acc 92.400 (92.400)
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2020-02-02 12:09:22 Epoch 59, Val iteration 19, acc 93.000 (92.600)
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* Prec: 92.60000114440918
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 24
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DPN92 0
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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=25, 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/1500/25
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:59:40 Iteration 0 Training Loss: 9.903e-01 Loss in Target Net: 1.334e+00
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2020-02-02 11:59:56 Iteration 50 Training Loss: 2.669e-01 Loss in Target Net: 6.546e-02
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2020-02-02 12:00:13 Iteration 100 Training Loss: 2.311e-01 Loss in Target Net: 4.025e-02
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2020-02-02 12:00:29 Iteration 150 Training Loss: 2.146e-01 Loss in Target Net: 4.924e-02
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2020-02-02 12:00:47 Iteration 200 Training Loss: 2.092e-01 Loss in Target Net: 3.597e-02
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2020-02-02 12:01:04 Iteration 250 Training Loss: 2.088e-01 Loss in Target Net: 3.310e-02
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2020-02-02 12:01:21 Iteration 300 Training Loss: 1.988e-01 Loss in Target Net: 2.816e-02
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2020-02-02 12:01:40 Iteration 350 Training Loss: 2.004e-01 Loss in Target Net: 3.195e-02
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2020-02-02 12:01:58 Iteration 400 Training Loss: 1.966e-01 Loss in Target Net: 4.594e-02
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2020-02-02 12:02:17 Iteration 450 Training Loss: 1.954e-01 Loss in Target Net: 3.676e-02
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2020-02-02 12:02:35 Iteration 500 Training Loss: 1.932e-01 Loss in Target Net: 3.307e-02
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2020-02-02 12:02:53 Iteration 550 Training Loss: 1.909e-01 Loss in Target Net: 3.325e-02
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2020-02-02 12:03:13 Iteration 600 Training Loss: 1.951e-01 Loss in Target Net: 2.945e-02
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2020-02-02 12:03:31 Iteration 650 Training Loss: 1.878e-01 Loss in Target Net: 2.998e-02
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2020-02-02 12:03:49 Iteration 700 Training Loss: 1.881e-01 Loss in Target Net: 3.243e-02
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