SunayBhat1/PureGen_Poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
2020-02-04 02:20:03 Epoch 59, Val iteration 19, acc 92.400 (93.080)
* Prec: 93.08000183105469
--------
------SUMMARY------
TIME ELAPSED (mins): 96
TARGET INDEX: 4
DPN92 1
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='8', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=40, 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-3Repeat/1500/40
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-04 04:26:06 Iteration 0 Training Loss: 9.892e-01 Loss in Target Net: 1.311e+00
2020-02-04 04:29:22 Iteration 50 Training Loss: 2.406e-01 Loss in Target Net: 8.356e-02
2020-02-04 04:32:42 Iteration 100 Training Loss: 2.104e-01 Loss in Target Net: 6.388e-02
2020-02-04 04:36:02 Iteration 150 Training Loss: 1.936e-01 Loss in Target Net: 6.047e-02
2020-02-04 04:39:23 Iteration 200 Training Loss: 1.864e-01 Loss in Target Net: 7.088e-02
2020-02-04 04:42:43 Iteration 250 Training Loss: 1.826e-01 Loss in Target Net: 6.202e-02
2020-02-04 04:46:04 Iteration 300 Training Loss: 1.774e-01 Loss in Target Net: 5.775e-02
2020-02-04 04:49:26 Iteration 350 Training Loss: 1.782e-01 Loss in Target Net: 5.494e-02
2020-02-04 04:52:47 Iteration 400 Training Loss: 1.732e-01 Loss in Target Net: 4.993e-02
2020-02-04 04:56:08 Iteration 450 Training Loss: 1.720e-01 Loss in Target Net: 5.834e-02
2020-02-04 04:59:31 Iteration 500 Training Loss: 1.715e-01 Loss in Target Net: 5.404e-02
2020-02-04 05:02:53 Iteration 550 Training Loss: 1.700e-01 Loss in Target Net: 5.464e-02
2020-02-04 05:06:15 Iteration 600 Training Loss: 1.661e-01 Loss in Target Net: 4.761e-02
2020-02-04 05:09:39 Iteration 650 Training Loss: 1.668e-01 Loss in Target Net: 4.740e-02
2020-02-04 05:13:01 Iteration 700 Training Loss: 1.641e-01 Loss in Target Net: 4.409e-02
2020-02-04 05:16:24 Iteration 750 Training Loss: 1.639e-01 Loss in Target Net: 4.647e-02
2020-02-04 05:19:46 Iteration 800 Training Loss: 1.677e-01 Loss in Target Net: 3.848e-02
2020-02-04 05:23:07 Iteration 850 Training Loss: 1.643e-01 Loss in Target Net: 4.378e-02
2020-02-04 05:26:29 Iteration 900 Training Loss: 1.648e-01 Loss in Target Net: 4.035e-02
2020-02-04 05:29:53 Iteration 950 Training Loss: 1.665e-01 Loss in Target Net: 4.449e-02
2020-02-04 05:33:15 Iteration 1000 Training Loss: 1.647e-01 Loss in Target Net: 4.229e-02
2020-02-04 05:36:40 Iteration 1050 Training Loss: 1.631e-01 Loss in Target Net: 4.321e-02
2020-02-04 05:40:05 Iteration 1100 Training Loss: 1.610e-01 Loss in Target Net: 4.253e-02
2020-02-04 05:43:28 Iteration 1150 Training Loss: 1.642e-01 Loss in Target Net: 3.750e-02
2020-02-04 05:46:51 Iteration 1200 Training Loss: 1.625e-01 Loss in Target Net: 3.867e-02
2020-02-04 05:50:29 Iteration 1250 Training Loss: 1.616e-01 Loss in Target Net: 4.008e-02
2020-02-04 05:54:10 Iteration 1300 Training Loss: 1.603e-01 Loss in Target Net: 4.016e-02
2020-02-04 05:57:27 Iteration 1350 Training Loss: 1.610e-01 Loss in Target Net: 4.436e-02
2020-02-04 06:00:30 Iteration 1400 Training Loss: 1.601e-01 Loss in Target Net: 4.321e-02
2020-02-04 06:03:25 Iteration 1450 Training Loss: 1.624e-01 Loss in Target Net: 3.886e-02
2020-02-04 06:06:14 Iteration 1499 Training Loss: 1.635e-01 Loss in Target Net: 4.258e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 06:07:18, Epoch 0, Iteration 7, loss 0.743 (0.625), acc 82.692 (87.600)
2020-02-04 06:12:03, 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:[-2.3285577, -2.7832801, 0.6985177, -1.4822518, -1.6491992, -2.7489836, 2.152069, -2.9124808, 11.3123, 0.28862834], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 06:17:52 Epoch 59, Val iteration 0, acc 91.600 (91.600)
2020-02-04 06:18:44 Epoch 59, Val iteration 19, acc 91.600 (92.820)
* Prec: 92.8200008392334
--------
------SUMMARY------
TIME ELAPSED (mins): 100
TARGET INDEX: 40
DPN92 1
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='9', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=41, 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-3Repeat/1500/41
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-04 04:24:03 Iteration 0 Training Loss: 1.034e+00 Loss in Target Net: 1.366e+00
2020-02-04 04:27:22 Iteration 50 Training Loss: 2.519e-01 Loss in Target Net: 1.519e-01
2020-02-04 04:30:42 Iteration 100 Training Loss: 2.241e-01 Loss in Target Net: 6.013e-02
2020-02-04 04:34:04 Iteration 150 Training Loss: 2.111e-01 Loss in Target Net: 6.604e-02
2020-02-04 04:37:29 Iteration 200 Training Loss: 2.023e-01 Loss in Target Net: 5.156e-02
2020-02-04 04:40:52 Iteration 250 Training Loss: 1.972e-01 Loss in Target Net: 5.358e-02
2020-02-04 04:44:15 Iteration 300 Training Loss: 1.959e-01 Loss in Target Net: 4.773e-02
2020-02-04 04:47:38 Iteration 350 Training Loss: 1.949e-01 Loss in Target Net: 5.691e-02
2020-02-04 04:51:01 Iteration 400 Training Loss: 1.880e-01 Loss in Target Net: 5.944e-02
2020-02-04 04:54:27 Iteration 450 Training Loss: 1.853e-01 Loss in Target Net: 5.524e-02
2020-02-04 04:57:53 Iteration 500 Training Loss: 1.866e-01 Loss in Target Net: 5.585e-02
2020-02-04 05:01:16 Iteration 550 Training Loss: 1.891e-01 Loss in Target Net: 5.401e-02
2020-02-04 05:04:41 Iteration 600 Training Loss: 1.881e-01 Loss in Target Net: 4.845e-02
2020-02-04 05:08:04 Iteration 650 Training Loss: 1.831e-01 Loss in Target Net: 4.816e-02
2020-02-04 05:11:27 Iteration 700 Training Loss: 1.838e-01 Loss in Target Net: 5.304e-02
2020-02-04 05:14:50 Iteration 750 Training Loss: 1.840e-01 Loss in Target Net: 5.236e-02
2020-02-04 05:18:14 Iteration 800 Training Loss: 1.842e-01 Loss in Target Net: 5.323e-02
2020-02-04 05:21:37 Iteration 850 Training Loss: 1.797e-01 Loss in Target Net: 4.774e-02
2020-02-04 05:25:06 Iteration 900 Training Loss: 1.795e-01 Loss in Target Net: 5.344e-02
2020-02-04 05:28:31 Iteration 950 Training Loss: 1.828e-01 Loss in Target Net: 5.193e-02
2020-02-04 05:31:55 Iteration 1000 Training Loss: 1.818e-01 Loss in Target Net: 5.301e-02
2020-02-04 05:35:19 Iteration 1050 Training Loss: 1.801e-01 Loss in Target Net: 5.883e-02
2020-02-04 05:38:47 Iteration 1100 Training Loss: 1.835e-01 Loss in Target Net: 5.672e-02
2020-02-04 05:42:14 Iteration 1150 Training Loss: 1.825e-01 Loss in Target Net: 4.723e-02
2020-02-04 05:45:36 Iteration 1200 Training Loss: 1.782e-01 Loss in Target Net: 5.149e-02
2020-02-04 05:49:09 Iteration 1250 Training Loss: 1.779e-01 Loss in Target Net: 6.410e-02
2020-02-04 05:52:48 Iteration 1300 Training Loss: 1.765e-01 Loss in Target Net: 4.539e-02
2020-02-04 05:56:20 Iteration 1350 Training Loss: 1.752e-01 Loss in Target Net: 5.169e-02
2020-02-04 05:59:33 Iteration 1400 Training Loss: 1.784e-01 Loss in Target Net: 5.167e-02
2020-02-04 06:02:36 Iteration 1450 Training Loss: 1.760e-01 Loss in Target Net: 4.882e-02
2020-02-04 06:05:31 Iteration 1499 Training Loss: 1.790e-01 Loss in Target Net: 5.011e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 06:06:26, Epoch 0, Iteration 7, loss 0.305 (0.432), acc 90.385 (89.600)
2020-02-04 06:11:08, 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:[-4.171242, 0.2146302, -1.836046, 1.5574557, -1.9527029, 0.5613123, 6.1930423, -1.4085413, 5.187201, -3.9605508], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 06:16:54 Epoch 59, Val iteration 0, acc 92.200 (92.200)
2020-02-04 06:17:46 Epoch 59, Val iteration 19, acc 93.600 (92.920)
* Prec: 92.92000160217285

2020-02-04 02:20:03 Epoch 59, Val iteration 19, acc 92.400 (93.080)

* Prec: 93.08000183105469

--------

------SUMMARY------

TIME ELAPSED (mins): 96

TARGET INDEX: 4

DPN92 1

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='8', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=40, 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-3Repeat/1500/40

Selected base image indices: [213, 225, 227, 247, 249]

2020-02-04 04:26:06 Iteration 0 Training Loss: 9.892e-01 Loss in Target Net: 1.311e+00

2020-02-04 04:29:22 Iteration 50 Training Loss: 2.406e-01 Loss in Target Net: 8.356e-02

2020-02-04 04:32:42 Iteration 100 Training Loss: 2.104e-01 Loss in Target Net: 6.388e-02

2020-02-04 04:36:02 Iteration 150 Training Loss: 1.936e-01 Loss in Target Net: 6.047e-02

2020-02-04 04:39:23 Iteration 200 Training Loss: 1.864e-01 Loss in Target Net: 7.088e-02

2020-02-04 04:42:43 Iteration 250 Training Loss: 1.826e-01 Loss in Target Net: 6.202e-02

2020-02-04 04:46:04 Iteration 300 Training Loss: 1.774e-01 Loss in Target Net: 5.775e-02

2020-02-04 04:49:26 Iteration 350 Training Loss: 1.782e-01 Loss in Target Net: 5.494e-02

2020-02-04 04:52:47 Iteration 400 Training Loss: 1.732e-01 Loss in Target Net: 4.993e-02

2020-02-04 04:56:08 Iteration 450 Training Loss: 1.720e-01 Loss in Target Net: 5.834e-02

2020-02-04 04:59:31 Iteration 500 Training Loss: 1.715e-01 Loss in Target Net: 5.404e-02

2020-02-04 05:02:53 Iteration 550 Training Loss: 1.700e-01 Loss in Target Net: 5.464e-02

2020-02-04 05:06:15 Iteration 600 Training Loss: 1.661e-01 Loss in Target Net: 4.761e-02

2020-02-04 05:09:39 Iteration 650 Training Loss: 1.668e-01 Loss in Target Net: 4.740e-02

2020-02-04 05:13:01 Iteration 700 Training Loss: 1.641e-01 Loss in Target Net: 4.409e-02

2020-02-04 05:16:24 Iteration 750 Training Loss: 1.639e-01 Loss in Target Net: 4.647e-02

2020-02-04 05:19:46 Iteration 800 Training Loss: 1.677e-01 Loss in Target Net: 3.848e-02

2020-02-04 05:23:07 Iteration 850 Training Loss: 1.643e-01 Loss in Target Net: 4.378e-02

2020-02-04 05:26:29 Iteration 900 Training Loss: 1.648e-01 Loss in Target Net: 4.035e-02

2020-02-04 05:29:53 Iteration 950 Training Loss: 1.665e-01 Loss in Target Net: 4.449e-02

2020-02-04 05:33:15 Iteration 1000 Training Loss: 1.647e-01 Loss in Target Net: 4.229e-02

2020-02-04 05:36:40 Iteration 1050 Training Loss: 1.631e-01 Loss in Target Net: 4.321e-02

2020-02-04 05:40:05 Iteration 1100 Training Loss: 1.610e-01 Loss in Target Net: 4.253e-02

2020-02-04 05:43:28 Iteration 1150 Training Loss: 1.642e-01 Loss in Target Net: 3.750e-02

2020-02-04 05:46:51 Iteration 1200 Training Loss: 1.625e-01 Loss in Target Net: 3.867e-02

2020-02-04 05:50:29 Iteration 1250 Training Loss: 1.616e-01 Loss in Target Net: 4.008e-02

2020-02-04 05:54:10 Iteration 1300 Training Loss: 1.603e-01 Loss in Target Net: 4.016e-02

2020-02-04 05:57:27 Iteration 1350 Training Loss: 1.610e-01 Loss in Target Net: 4.436e-02

2020-02-04 06:00:30 Iteration 1400 Training Loss: 1.601e-01 Loss in Target Net: 4.321e-02

2020-02-04 06:03:25 Iteration 1450 Training Loss: 1.624e-01 Loss in Target Net: 3.886e-02

2020-02-04 06:06:14 Iteration 1499 Training Loss: 1.635e-01 Loss in Target Net: 4.258e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-04 06:07:18, Epoch 0, Iteration 7, loss 0.743 (0.625), acc 82.692 (87.600)

2020-02-04 06:12:03, 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:[-2.3285577, -2.7832801, 0.6985177, -1.4822518, -1.6491992, -2.7489836, 2.152069, -2.9124808, 11.3123, 0.28862834], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-04 06:17:52 Epoch 59, Val iteration 0, acc 91.600 (91.600)

2020-02-04 06:18:44 Epoch 59, Val iteration 19, acc 91.600 (92.820)

* Prec: 92.8200008392334

--------

------SUMMARY------

TIME ELAPSED (mins): 100

TARGET INDEX: 40

DPN92 1

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='9', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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=41, 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-3Repeat/1500/41

Selected base image indices: [213, 225, 227, 247, 249]

2020-02-04 04:24:03 Iteration 0 Training Loss: 1.034e+00 Loss in Target Net: 1.366e+00

2020-02-04 04:27:22 Iteration 50 Training Loss: 2.519e-01 Loss in Target Net: 1.519e-01

2020-02-04 04:30:42 Iteration 100 Training Loss: 2.241e-01 Loss in Target Net: 6.013e-02

2020-02-04 04:34:04 Iteration 150 Training Loss: 2.111e-01 Loss in Target Net: 6.604e-02

2020-02-04 04:37:29 Iteration 200 Training Loss: 2.023e-01 Loss in Target Net: 5.156e-02

2020-02-04 04:40:52 Iteration 250 Training Loss: 1.972e-01 Loss in Target Net: 5.358e-02

2020-02-04 04:44:15 Iteration 300 Training Loss: 1.959e-01 Loss in Target Net: 4.773e-02

2020-02-04 04:47:38 Iteration 350 Training Loss: 1.949e-01 Loss in Target Net: 5.691e-02

2020-02-04 04:51:01 Iteration 400 Training Loss: 1.880e-01 Loss in Target Net: 5.944e-02

2020-02-04 04:54:27 Iteration 450 Training Loss: 1.853e-01 Loss in Target Net: 5.524e-02

2020-02-04 04:57:53 Iteration 500 Training Loss: 1.866e-01 Loss in Target Net: 5.585e-02

2020-02-04 05:01:16 Iteration 550 Training Loss: 1.891e-01 Loss in Target Net: 5.401e-02

2020-02-04 05:04:41 Iteration 600 Training Loss: 1.881e-01 Loss in Target Net: 4.845e-02

2020-02-04 05:08:04 Iteration 650 Training Loss: 1.831e-01 Loss in Target Net: 4.816e-02

2020-02-04 05:11:27 Iteration 700 Training Loss: 1.838e-01 Loss in Target Net: 5.304e-02

2020-02-04 05:14:50 Iteration 750 Training Loss: 1.840e-01 Loss in Target Net: 5.236e-02

2020-02-04 05:18:14 Iteration 800 Training Loss: 1.842e-01 Loss in Target Net: 5.323e-02

2020-02-04 05:21:37 Iteration 850 Training Loss: 1.797e-01 Loss in Target Net: 4.774e-02

2020-02-04 05:25:06 Iteration 900 Training Loss: 1.795e-01 Loss in Target Net: 5.344e-02

2020-02-04 05:28:31 Iteration 950 Training Loss: 1.828e-01 Loss in Target Net: 5.193e-02

2020-02-04 05:31:55 Iteration 1000 Training Loss: 1.818e-01 Loss in Target Net: 5.301e-02

2020-02-04 05:35:19 Iteration 1050 Training Loss: 1.801e-01 Loss in Target Net: 5.883e-02

2020-02-04 05:38:47 Iteration 1100 Training Loss: 1.835e-01 Loss in Target Net: 5.672e-02

2020-02-04 05:42:14 Iteration 1150 Training Loss: 1.825e-01 Loss in Target Net: 4.723e-02

2020-02-04 05:45:36 Iteration 1200 Training Loss: 1.782e-01 Loss in Target Net: 5.149e-02

2020-02-04 05:49:09 Iteration 1250 Training Loss: 1.779e-01 Loss in Target Net: 6.410e-02

2020-02-04 05:52:48 Iteration 1300 Training Loss: 1.765e-01 Loss in Target Net: 4.539e-02

2020-02-04 05:56:20 Iteration 1350 Training Loss: 1.752e-01 Loss in Target Net: 5.169e-02

2020-02-04 05:59:33 Iteration 1400 Training Loss: 1.784e-01 Loss in Target Net: 5.167e-02

2020-02-04 06:02:36 Iteration 1450 Training Loss: 1.760e-01 Loss in Target Net: 4.882e-02

2020-02-04 06:05:31 Iteration 1499 Training Loss: 1.790e-01 Loss in Target Net: 5.011e-02

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-04 06:06:26, Epoch 0, Iteration 7, loss 0.305 (0.432), acc 90.385 (89.600)

2020-02-04 06:11:08, 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:[-4.171242, 0.2146302, -1.836046, 1.5574557, -1.9527029, 0.5613123, 6.1930423, -1.4085413, 5.187201, -3.9605508], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-04 06:16:54 Epoch 59, Val iteration 0, acc 92.200 (92.200)

2020-02-04 06:17:46 Epoch 59, Val iteration 19, acc 93.600 (92.920)

* Prec: 92.92000160217285