Epoch: 0001 train_loss= 2.07793 train_acc= 0.14825 val_loss= 2.08278 val_acc= 0.17241 time= 0.91373
Epoch: 0002 train_loss= 2.07691 train_acc= 0.15633 val_loss= 2.08238 val_acc= 0.17241 time= 0.00000
Epoch: 0003 train_loss= 2.07591 train_acc= 0.15903 val_loss= 2.08190 val_acc= 0.06897 time= 0.00000
Epoch: 0004 train_loss= 2.07484 train_acc= 0.15094 val_loss= 2.08129 val_acc= 0.03448 time= 0.01563
Epoch: 0005 train_loss= 2.07254 train_acc= 0.16712 val_loss= 2.08050 val_acc= 0.06897 time= 0.00000
Epoch: 0006 train_loss= 2.07225 train_acc= 0.15903 val_loss= 2.07958 val_acc= 0.06897 time= 0.00000
Epoch: 0007 train_loss= 2.07008 train_acc= 0.18059 val_loss= 2.07849 val_acc= 0.06897 time= 0.01563
Epoch: 0008 train_loss= 2.06767 train_acc= 0.18329 val_loss= 2.07708 val_acc= 0.03448 time= 0.00000
Epoch: 0009 train_loss= 2.06970 train_acc= 0.14825 val_loss= 2.07548 val_acc= 0.06897 time= 0.00000
Epoch: 0010 train_loss= 2.06688 train_acc= 0.17251 val_loss= 2.07365 val_acc= 0.13793 time= 0.01562
Epoch: 0011 train_loss= 2.06558 train_acc= 0.16173 val_loss= 2.07160 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.06648 train_acc= 0.15903 val_loss= 2.06938 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.06470 train_acc= 0.15903 val_loss= 2.06708 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.06348 train_acc= 0.15364 val_loss= 2.06469 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.06071 train_acc= 0.15364 val_loss= 2.06216 val_acc= 0.17241 time= 0.00000
Epoch: 0016 train_loss= 2.05902 train_acc= 0.15903 val_loss= 2.05958 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.05978 train_acc= 0.14555 val_loss= 2.05709 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.05898 train_acc= 0.15364 val_loss= 2.05469 val_acc= 0.17241 time= 0.00000
Epoch: 0019 train_loss= 2.05789 train_acc= 0.15364 val_loss= 2.05257 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 2.05764 train_acc= 0.15364 val_loss= 2.05075 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.05510 train_acc= 0.14825 val_loss= 2.04925 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.05583 train_acc= 0.15094 val_loss= 2.04805 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.05242 train_acc= 0.15364 val_loss= 2.04723 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.05496 train_acc= 0.14286 val_loss= 2.04674 val_acc= 0.17241 time= 0.00000
Epoch: 0025 train_loss= 2.05216 train_acc= 0.14555 val_loss= 2.04664 val_acc= 0.17241 time= 0.01563
Epoch: 0026 train_loss= 2.05315 train_acc= 0.14555 val_loss= 2.04668 val_acc= 0.13793 time= 0.00000
Epoch: 0027 train_loss= 2.05245 train_acc= 0.17520 val_loss= 2.04728 val_acc= 0.06897 time= 0.00000
Epoch: 0028 train_loss= 2.05282 train_acc= 0.16981 val_loss= 2.04816 val_acc= 0.06897 time= 0.01563
Epoch: 0029 train_loss= 2.04925 train_acc= 0.18059 val_loss= 2.04941 val_acc= 0.06897 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08474 accuracy= 0.15254 time= 0.00000 
