Epoch: 0001 train_loss= 2.16126 train_acc= 0.11321 val_loss= 2.03512 val_acc= 0.27586 time= 0.62891
Epoch: 0002 train_loss= 2.11655 train_acc= 0.16226 val_loss= 2.10062 val_acc= 0.03448 time= 0.01563
Epoch: 0003 train_loss= 2.09684 train_acc= 0.13962 val_loss= 2.13541 val_acc= 0.00000 time= 0.01563
Epoch: 0004 train_loss= 2.06196 train_acc= 0.15849 val_loss= 2.14449 val_acc= 0.00000 time= 0.01563
Epoch: 0005 train_loss= 2.07751 train_acc= 0.17736 val_loss= 2.14215 val_acc= 0.00000 time= 0.02400
Epoch: 0006 train_loss= 2.05856 train_acc= 0.16604 val_loss= 2.13747 val_acc= 0.03448 time= 0.01700
Epoch: 0007 train_loss= 2.07251 train_acc= 0.18113 val_loss= 2.13149 val_acc= 0.03448 time= 0.01500
Epoch: 0008 train_loss= 2.06119 train_acc= 0.15849 val_loss= 2.12587 val_acc= 0.03448 time= 0.01500
Epoch: 0009 train_loss= 2.06123 train_acc= 0.14717 val_loss= 2.12158 val_acc= 0.03448 time= 0.01600
Epoch: 0010 train_loss= 2.04841 train_acc= 0.13962 val_loss= 2.11617 val_acc= 0.03448 time= 0.01700
Epoch: 0011 train_loss= 2.03969 train_acc= 0.18113 val_loss= 2.10825 val_acc= 0.03448 time= 0.01400
Epoch: 0012 train_loss= 2.04839 train_acc= 0.15849 val_loss= 2.09765 val_acc= 0.06897 time= 0.01300
Epoch: 0013 train_loss= 2.04295 train_acc= 0.16226 val_loss= 2.08318 val_acc= 0.03448 time= 0.01500
Epoch: 0014 train_loss= 2.04898 train_acc= 0.16604 val_loss= 2.06822 val_acc= 0.13793 time= 0.01500
Epoch: 0015 train_loss= 2.03636 train_acc= 0.17358 val_loss= 2.05039 val_acc= 0.20690 time= 0.01300
Epoch: 0016 train_loss= 2.04556 train_acc= 0.15472 val_loss= 2.03533 val_acc= 0.27586 time= 0.01400
Epoch: 0017 train_loss= 2.03278 train_acc= 0.21887 val_loss= 2.02322 val_acc= 0.31034 time= 0.01300
Epoch: 0018 train_loss= 2.04618 train_acc= 0.17736 val_loss= 2.01732 val_acc= 0.31034 time= 0.01200
Epoch: 0019 train_loss= 2.01997 train_acc= 0.18113 val_loss= 2.01671 val_acc= 0.34483 time= 0.01200
Epoch: 0020 train_loss= 2.02601 train_acc= 0.21887 val_loss= 2.01659 val_acc= 0.34483 time= 0.01400
Epoch: 0021 train_loss= 2.03971 train_acc= 0.16226 val_loss= 2.02021 val_acc= 0.31034 time= 0.01500
Epoch: 0022 train_loss= 2.01926 train_acc= 0.20755 val_loss= 2.02749 val_acc= 0.27586 time= 0.01300
Epoch: 0023 train_loss= 2.04069 train_acc= 0.15094 val_loss= 2.03715 val_acc= 0.27586 time= 0.01400
Early stopping...
Optimization Finished!
Test set results: cost= 2.08858 accuracy= 0.11864 time= 0.00600 
