Epoch: 0001 train_loss= 1.38886 train_acc= 0.28013 val_loss= 1.36808 val_acc= 0.28571 time= 0.15626
Epoch: 0002 train_loss= 1.37674 train_acc= 0.27687 val_loss= 1.37205 val_acc= 0.25000 time= 0.01563
Epoch: 0003 train_loss= 1.38393 train_acc= 0.30945 val_loss= 1.37602 val_acc= 0.25000 time= 0.01562
Epoch: 0004 train_loss= 1.38404 train_acc= 0.23453 val_loss= 1.38048 val_acc= 0.26786 time= 0.01563
Epoch: 0005 train_loss= 1.36575 train_acc= 0.32899 val_loss= 1.38605 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.36395 train_acc= 0.34853 val_loss= 1.39076 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.37305 train_acc= 0.28990 val_loss= 1.39525 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.36588 train_acc= 0.29316 val_loss= 1.39974 val_acc= 0.28571 time= 0.00000
Epoch: 0009 train_loss= 1.36214 train_acc= 0.31270 val_loss= 1.40413 val_acc= 0.23214 time= 0.01563
Epoch: 0010 train_loss= 1.35918 train_acc= 0.30945 val_loss= 1.40818 val_acc= 0.23214 time= 0.01562
Epoch: 0011 train_loss= 1.36062 train_acc= 0.35179 val_loss= 1.41119 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.35989 train_acc= 0.31596 val_loss= 1.41352 val_acc= 0.23214 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39428 accuracy= 0.27434 time= 0.00000 
