Epoch: 0001 train_loss= 2.08728 train_acc= 0.11698 val_loss= 2.08428 val_acc= 0.27586 time= 0.26564
Epoch: 0002 train_loss= 2.08503 train_acc= 0.13962 val_loss= 2.08143 val_acc= 0.27586 time= 0.00000
Epoch: 0003 train_loss= 2.08292 train_acc= 0.13585 val_loss= 2.07861 val_acc= 0.27586 time= 0.00000
Epoch: 0004 train_loss= 2.08108 train_acc= 0.13585 val_loss= 2.07576 val_acc= 0.27586 time= 0.01563
Epoch: 0005 train_loss= 2.07913 train_acc= 0.13585 val_loss= 2.07291 val_acc= 0.27586 time= 0.00000
Epoch: 0006 train_loss= 2.07767 train_acc= 0.13962 val_loss= 2.07000 val_acc= 0.27586 time= 0.01563
Epoch: 0007 train_loss= 2.07616 train_acc= 0.13208 val_loss= 2.06691 val_acc= 0.27586 time= 0.00000
Epoch: 0008 train_loss= 2.07552 train_acc= 0.12830 val_loss= 2.06382 val_acc= 0.27586 time= 0.01563
Epoch: 0009 train_loss= 2.07410 train_acc= 0.13208 val_loss= 2.06075 val_acc= 0.27586 time= 0.01563
Epoch: 0010 train_loss= 2.07294 train_acc= 0.13585 val_loss= 2.05782 val_acc= 0.27586 time= 0.00000
Epoch: 0011 train_loss= 2.07218 train_acc= 0.13585 val_loss= 2.05513 val_acc= 0.27586 time= 0.01563
Epoch: 0012 train_loss= 2.07153 train_acc= 0.13585 val_loss= 2.05262 val_acc= 0.27586 time= 0.00000
Epoch: 0013 train_loss= 2.07077 train_acc= 0.12830 val_loss= 2.05044 val_acc= 0.13793 time= 0.01563
Epoch: 0014 train_loss= 2.07065 train_acc= 0.13962 val_loss= 2.04851 val_acc= 0.10345 time= 0.01562
Epoch: 0015 train_loss= 2.06961 train_acc= 0.14717 val_loss= 2.04687 val_acc= 0.10345 time= 0.00000
Epoch: 0016 train_loss= 2.06882 train_acc= 0.15849 val_loss= 2.04541 val_acc= 0.10345 time= 0.01563
Epoch: 0017 train_loss= 2.06851 train_acc= 0.15849 val_loss= 2.04425 val_acc= 0.10345 time= 0.00000
Epoch: 0018 train_loss= 2.06747 train_acc= 0.15849 val_loss= 2.04320 val_acc= 0.10345 time= 0.01563
Epoch: 0019 train_loss= 2.06669 train_acc= 0.15849 val_loss= 2.04240 val_acc= 0.10345 time= 0.00000
Epoch: 0020 train_loss= 2.06642 train_acc= 0.15849 val_loss= 2.04182 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.06538 train_acc= 0.15849 val_loss= 2.04136 val_acc= 0.10345 time= 0.01563
Epoch: 0022 train_loss= 2.06452 train_acc= 0.15849 val_loss= 2.04116 val_acc= 0.10345 time= 0.00000
Epoch: 0023 train_loss= 2.06508 train_acc= 0.15849 val_loss= 2.04112 val_acc= 0.10345 time= 0.01563
Epoch: 0024 train_loss= 2.06475 train_acc= 0.15849 val_loss= 2.04113 val_acc= 0.10345 time= 0.00000
Epoch: 0025 train_loss= 2.06345 train_acc= 0.15849 val_loss= 2.04119 val_acc= 0.10345 time= 0.01563
Epoch: 0026 train_loss= 2.06384 train_acc= 0.15849 val_loss= 2.04118 val_acc= 0.10345 time= 0.00000
Epoch: 0027 train_loss= 2.06336 train_acc= 0.15849 val_loss= 2.04096 val_acc= 0.10345 time= 0.01562
Epoch: 0028 train_loss= 2.06286 train_acc= 0.15849 val_loss= 2.04047 val_acc= 0.10345 time= 0.01563
Epoch: 0029 train_loss= 2.06162 train_acc= 0.15849 val_loss= 2.03974 val_acc= 0.10345 time= 0.00000
Epoch: 0030 train_loss= 2.06159 train_acc= 0.15849 val_loss= 2.03889 val_acc= 0.10345 time= 0.01563
Epoch: 0031 train_loss= 2.06236 train_acc= 0.15849 val_loss= 2.03794 val_acc= 0.10345 time= 0.00000
Epoch: 0032 train_loss= 2.06080 train_acc= 0.15849 val_loss= 2.03681 val_acc= 0.10345 time= 0.01563
Epoch: 0033 train_loss= 2.06073 train_acc= 0.15849 val_loss= 2.03536 val_acc= 0.10345 time= 0.01563
Epoch: 0034 train_loss= 2.06133 train_acc= 0.15849 val_loss= 2.03404 val_acc= 0.10345 time= 0.00000
Epoch: 0035 train_loss= 2.05992 train_acc= 0.15849 val_loss= 2.03279 val_acc= 0.10345 time= 0.01563
Epoch: 0036 train_loss= 2.05994 train_acc= 0.15849 val_loss= 2.03171 val_acc= 0.10345 time= 0.00000
Epoch: 0037 train_loss= 2.05987 train_acc= 0.15849 val_loss= 2.03075 val_acc= 0.10345 time= 0.01563
Epoch: 0038 train_loss= 2.05908 train_acc= 0.15849 val_loss= 2.02973 val_acc= 0.10345 time= 0.00000
Epoch: 0039 train_loss= 2.06016 train_acc= 0.15849 val_loss= 2.02890 val_acc= 0.10345 time= 0.01563
Epoch: 0040 train_loss= 2.05950 train_acc= 0.15849 val_loss= 2.02829 val_acc= 0.10345 time= 0.01563
Epoch: 0041 train_loss= 2.05896 train_acc= 0.15849 val_loss= 2.02771 val_acc= 0.10345 time= 0.00000
Epoch: 0042 train_loss= 2.05851 train_acc= 0.15849 val_loss= 2.02732 val_acc= 0.10345 time= 0.01563
Epoch: 0043 train_loss= 2.05903 train_acc= 0.15849 val_loss= 2.02713 val_acc= 0.10345 time= 0.00000
Epoch: 0044 train_loss= 2.05903 train_acc= 0.15849 val_loss= 2.02734 val_acc= 0.10345 time= 0.01563
Epoch: 0045 train_loss= 2.05841 train_acc= 0.15849 val_loss= 2.02766 val_acc= 0.10345 time= 0.01563
Epoch: 0046 train_loss= 2.05930 train_acc= 0.15849 val_loss= 2.02754 val_acc= 0.10345 time= 0.00000
Epoch: 0047 train_loss= 2.05804 train_acc= 0.15849 val_loss= 2.02732 val_acc= 0.10345 time= 0.01563
Epoch: 0048 train_loss= 2.05724 train_acc= 0.15849 val_loss= 2.02719 val_acc= 0.10345 time= 0.00000
Epoch: 0049 train_loss= 2.05792 train_acc= 0.15849 val_loss= 2.02676 val_acc= 0.10345 time= 0.01563
Epoch: 0050 train_loss= 2.05832 train_acc= 0.15849 val_loss= 2.02602 val_acc= 0.10345 time= 0.00000
Epoch: 0051 train_loss= 2.05821 train_acc= 0.15849 val_loss= 2.02523 val_acc= 0.10345 time= 0.01563
Epoch: 0052 train_loss= 2.05839 train_acc= 0.15849 val_loss= 2.02467 val_acc= 0.10345 time= 0.01563
Epoch: 0053 train_loss= 2.05788 train_acc= 0.15849 val_loss= 2.02435 val_acc= 0.10345 time= 0.00000
Epoch: 0054 train_loss= 2.05728 train_acc= 0.15849 val_loss= 2.02397 val_acc= 0.10345 time= 0.01563
Epoch: 0055 train_loss= 2.05768 train_acc= 0.15849 val_loss= 2.02333 val_acc= 0.10345 time= 0.00000
Epoch: 0056 train_loss= 2.05695 train_acc= 0.15849 val_loss= 2.02253 val_acc= 0.10345 time= 0.01563
Epoch: 0057 train_loss= 2.05764 train_acc= 0.15849 val_loss= 2.02185 val_acc= 0.10345 time= 0.00000
Epoch: 0058 train_loss= 2.05720 train_acc= 0.15849 val_loss= 2.02144 val_acc= 0.10345 time= 0.01563
Epoch: 0059 train_loss= 2.05689 train_acc= 0.15849 val_loss= 2.02171 val_acc= 0.10345 time= 0.01563
Epoch: 0060 train_loss= 2.05565 train_acc= 0.15849 val_loss= 2.02196 val_acc= 0.10345 time= 0.00000
Epoch: 0061 train_loss= 2.05650 train_acc= 0.15849 val_loss= 2.02204 val_acc= 0.10345 time= 0.01563
Epoch: 0062 train_loss= 2.05645 train_acc= 0.15849 val_loss= 2.02213 val_acc= 0.10345 time= 0.00000
Epoch: 0063 train_loss= 2.05633 train_acc= 0.15849 val_loss= 2.02271 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.02775 accuracy= 0.16949 time= 0.00000 
