Epoch: 0001 train_loss= 2.08962 train_acc= 0.10243 val_loss= 2.08863 val_acc= 0.03448 time= 0.28127
Epoch: 0002 train_loss= 2.09401 train_acc= 0.09973 val_loss= 2.08751 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.07149 train_acc= 0.15364 val_loss= 2.08662 val_acc= 0.06897 time= 0.00000
Epoch: 0004 train_loss= 2.07628 train_acc= 0.15633 val_loss= 2.08564 val_acc= 0.03448 time= 0.01563
Epoch: 0005 train_loss= 2.07462 train_acc= 0.15094 val_loss= 2.08517 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07183 train_acc= 0.14016 val_loss= 2.08491 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.06561 train_acc= 0.14555 val_loss= 2.08523 val_acc= 0.10345 time= 0.01563
Epoch: 0008 train_loss= 2.06623 train_acc= 0.15633 val_loss= 2.08580 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.06583 train_acc= 0.12938 val_loss= 2.08643 val_acc= 0.17241 time= 0.01562
Epoch: 0010 train_loss= 2.06342 train_acc= 0.14825 val_loss= 2.08739 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.06013 train_acc= 0.18598 val_loss= 2.08858 val_acc= 0.17241 time= 0.01563
Epoch: 0012 train_loss= 2.05761 train_acc= 0.15903 val_loss= 2.09012 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07659 accuracy= 0.15254 time= 0.00000 
