Epoch: 0001 train_loss= 2.08425 train_acc= 0.13208 val_loss= 2.10500 val_acc= 0.03448 time= 0.17188
Epoch: 0002 train_loss= 2.07595 train_acc= 0.11698 val_loss= 2.10760 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.06251 train_acc= 0.16226 val_loss= 2.11016 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.06327 train_acc= 0.16604 val_loss= 2.11342 val_acc= 0.06897 time= 0.01563
Epoch: 0005 train_loss= 2.05954 train_acc= 0.14340 val_loss= 2.11727 val_acc= 0.06897 time= 0.01563
Epoch: 0006 train_loss= 2.06308 train_acc= 0.11698 val_loss= 2.12173 val_acc= 0.06897 time= 0.00000
Epoch: 0007 train_loss= 2.06451 train_acc= 0.16226 val_loss= 2.12659 val_acc= 0.06897 time= 0.01563
Epoch: 0008 train_loss= 2.04877 train_acc= 0.17358 val_loss= 2.13176 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.05122 train_acc= 0.16981 val_loss= 2.13707 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.04876 train_acc= 0.15472 val_loss= 2.14226 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.04431 train_acc= 0.16604 val_loss= 2.14671 val_acc= 0.10345 time= 0.01562
Epoch: 0012 train_loss= 2.04112 train_acc= 0.18491 val_loss= 2.15040 val_acc= 0.10345 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.14828 accuracy= 0.13559 time= 0.01563 
