Epoch: 0001 train_loss= 1.46071 train_acc= 0.24219 val_loss= 1.41034 val_acc= 0.28571 time= 0.52785
Epoch: 0002 train_loss= 1.47955 train_acc= 0.23828 val_loss= 1.40194 val_acc= 0.28571 time= 0.01700
Epoch: 0003 train_loss= 1.42384 train_acc= 0.23047 val_loss= 1.39591 val_acc= 0.28571 time= 0.01600
Epoch: 0004 train_loss= 1.43157 train_acc= 0.24414 val_loss= 1.39173 val_acc= 0.32143 time= 0.01700
Epoch: 0005 train_loss= 1.42419 train_acc= 0.25781 val_loss= 1.38878 val_acc= 0.26786 time= 0.01600
Epoch: 0006 train_loss= 1.40735 train_acc= 0.22070 val_loss= 1.38630 val_acc= 0.21429 time= 0.01700
Epoch: 0007 train_loss= 1.38585 train_acc= 0.25586 val_loss= 1.38426 val_acc= 0.26786 time= 0.01600
Epoch: 0008 train_loss= 1.38661 train_acc= 0.30664 val_loss= 1.38223 val_acc= 0.28571 time= 0.01600
Epoch: 0009 train_loss= 1.39798 train_acc= 0.24219 val_loss= 1.37996 val_acc= 0.26786 time= 0.01710
Epoch: 0010 train_loss= 1.38605 train_acc= 0.29297 val_loss= 1.37775 val_acc= 0.30357 time= 0.01437
Epoch: 0011 train_loss= 1.37237 train_acc= 0.32227 val_loss= 1.37572 val_acc= 0.32143 time= 0.01500
Epoch: 0012 train_loss= 1.37432 train_acc= 0.31445 val_loss= 1.37388 val_acc= 0.30357 time= 0.01410
Epoch: 0013 train_loss= 1.38762 train_acc= 0.31055 val_loss= 1.37155 val_acc= 0.30357 time= 0.01512
Epoch: 0014 train_loss= 1.37037 train_acc= 0.34570 val_loss= 1.36948 val_acc= 0.30357 time= 0.01725
Epoch: 0015 train_loss= 1.39164 train_acc= 0.34180 val_loss= 1.36750 val_acc= 0.30357 time= 0.01412
Epoch: 0016 train_loss= 1.36355 train_acc= 0.34180 val_loss= 1.36602 val_acc= 0.30357 time= 0.01411
Epoch: 0017 train_loss= 1.36803 train_acc= 0.33789 val_loss= 1.36507 val_acc= 0.30357 time= 0.01638
Epoch: 0018 train_loss= 1.37329 train_acc= 0.33008 val_loss= 1.36432 val_acc= 0.30357 time= 0.00895
Epoch: 0019 train_loss= 1.37249 train_acc= 0.34180 val_loss= 1.36397 val_acc= 0.30357 time= 0.01289
Epoch: 0020 train_loss= 1.36218 train_acc= 0.33008 val_loss= 1.36397 val_acc= 0.30357 time= 0.02353
Epoch: 0021 train_loss= 1.35999 train_acc= 0.32617 val_loss= 1.36409 val_acc= 0.30357 time= 0.01309
Epoch: 0022 train_loss= 1.36355 train_acc= 0.33398 val_loss= 1.36424 val_acc= 0.30357 time= 0.01400
Epoch: 0023 train_loss= 1.36253 train_acc= 0.32812 val_loss= 1.36442 val_acc= 0.30357 time= 0.01300
Epoch: 0024 train_loss= 1.37799 train_acc= 0.33398 val_loss= 1.36452 val_acc= 0.30357 time= 0.01400
Epoch: 0025 train_loss= 1.37592 train_acc= 0.33789 val_loss= 1.36437 val_acc= 0.30357 time= 0.01400
Epoch: 0026 train_loss= 1.36890 train_acc= 0.33398 val_loss= 1.36430 val_acc= 0.30357 time= 0.00866
Epoch: 0027 train_loss= 1.35614 train_acc= 0.34570 val_loss= 1.36439 val_acc= 0.30357 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.41469 accuracy= 0.31858 time= 0.00000 
