Epoch: 0001 train_loss= 1.40779 train_acc= 0.29190 val_loss= 1.40072 val_acc= 0.30357 time= 0.73442
Epoch: 0002 train_loss= 1.39881 train_acc= 0.29330 val_loss= 1.39751 val_acc= 0.30357 time= 0.01562
Epoch: 0003 train_loss= 1.38339 train_acc= 0.31564 val_loss= 1.39465 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.38697 train_acc= 0.30866 val_loss= 1.39245 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38830 train_acc= 0.31285 val_loss= 1.39096 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38567 train_acc= 0.31844 val_loss= 1.39014 val_acc= 0.28571 time= 0.01562
Epoch: 0007 train_loss= 1.37958 train_acc= 0.31425 val_loss= 1.39013 val_acc= 0.28571 time= 0.00000
Epoch: 0008 train_loss= 1.38088 train_acc= 0.30587 val_loss= 1.39036 val_acc= 0.28571 time= 0.02073
Epoch: 0009 train_loss= 1.37804 train_acc= 0.32123 val_loss= 1.39093 val_acc= 0.28571 time= 0.01100
Epoch: 0010 train_loss= 1.37673 train_acc= 0.31145 val_loss= 1.39210 val_acc= 0.25000 time= 0.01563
Epoch: 0011 train_loss= 1.38297 train_acc= 0.31006 val_loss= 1.39328 val_acc= 0.23214 time= 0.01563
Epoch: 0012 train_loss= 1.38173 train_acc= 0.29469 val_loss= 1.39388 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.40451 accuracy= 0.28319 time= 0.00000 
