Epoch: 0001 train_loss= 1.39378 train_acc= 0.19531 val_loss= 1.39178 val_acc= 0.18333 time= 0.25747
Epoch: 0002 train_loss= 1.39248 train_acc= 0.19531 val_loss= 1.39065 val_acc= 0.18333 time= 0.01562
Epoch: 0003 train_loss= 1.39152 train_acc= 0.19922 val_loss= 1.38960 val_acc= 0.36667 time= 0.01563
Epoch: 0004 train_loss= 1.39057 train_acc= 0.30469 val_loss= 1.38887 val_acc= 0.36667 time= 0.01563
Epoch: 0005 train_loss= 1.38981 train_acc= 0.30469 val_loss= 1.38812 val_acc= 0.36667 time= 0.01563
Epoch: 0006 train_loss= 1.38920 train_acc= 0.30469 val_loss= 1.38724 val_acc= 0.36667 time= 0.01563
Epoch: 0007 train_loss= 1.38867 train_acc= 0.30469 val_loss= 1.38633 val_acc= 0.36667 time= 0.01563
Epoch: 0008 train_loss= 1.38814 train_acc= 0.30469 val_loss= 1.38540 val_acc= 0.36667 time= 0.01562
Epoch: 0009 train_loss= 1.38756 train_acc= 0.30469 val_loss= 1.38443 val_acc= 0.36667 time= 0.01563
Epoch: 0010 train_loss= 1.38719 train_acc= 0.30469 val_loss= 1.38343 val_acc= 0.36667 time= 0.01563
Epoch: 0011 train_loss= 1.38654 train_acc= 0.30469 val_loss= 1.38242 val_acc= 0.36667 time= 0.01563
Epoch: 0012 train_loss= 1.38594 train_acc= 0.30469 val_loss= 1.38139 val_acc= 0.36667 time= 0.01563
Epoch: 0013 train_loss= 1.38533 train_acc= 0.30469 val_loss= 1.38031 val_acc= 0.36667 time= 0.01563
Epoch: 0014 train_loss= 1.38452 train_acc= 0.30469 val_loss= 1.37899 val_acc= 0.36667 time= 0.01563
Epoch: 0015 train_loss= 1.38451 train_acc= 0.30469 val_loss= 1.37755 val_acc= 0.36667 time= 0.01563
Epoch: 0016 train_loss= 1.38330 train_acc= 0.30469 val_loss= 1.37600 val_acc= 0.36667 time= 0.01563
Epoch: 0017 train_loss= 1.38301 train_acc= 0.30469 val_loss= 1.37438 val_acc= 0.36667 time= 0.01563
Epoch: 0018 train_loss= 1.38250 train_acc= 0.30469 val_loss= 1.37274 val_acc= 0.36667 time= 0.01563
Epoch: 0019 train_loss= 1.38179 train_acc= 0.30469 val_loss= 1.37108 val_acc= 0.36667 time= 0.03125
Epoch: 0020 train_loss= 1.38117 train_acc= 0.30469 val_loss= 1.36943 val_acc= 0.36667 time= 0.01563
Epoch: 0021 train_loss= 1.38103 train_acc= 0.30469 val_loss= 1.36783 val_acc= 0.36667 time= 0.01563
Epoch: 0022 train_loss= 1.38066 train_acc= 0.30469 val_loss= 1.36628 val_acc= 0.36667 time= 0.01563
Epoch: 0023 train_loss= 1.38064 train_acc= 0.30469 val_loss= 1.36481 val_acc= 0.36667 time= 0.01563
Epoch: 0024 train_loss= 1.38022 train_acc= 0.30469 val_loss= 1.36340 val_acc= 0.36667 time= 0.03125
Epoch: 0025 train_loss= 1.37916 train_acc= 0.30469 val_loss= 1.36206 val_acc= 0.36667 time= 0.01563
Epoch: 0026 train_loss= 1.37896 train_acc= 0.30469 val_loss= 1.36079 val_acc= 0.36667 time= 0.01563
Epoch: 0027 train_loss= 1.37830 train_acc= 0.30469 val_loss= 1.35962 val_acc= 0.36667 time= 0.01563
Epoch: 0028 train_loss= 1.37771 train_acc= 0.30469 val_loss= 1.35855 val_acc= 0.36667 time= 0.01563
Epoch: 0029 train_loss= 1.37798 train_acc= 0.30469 val_loss= 1.35766 val_acc= 0.36667 time= 0.03125
Epoch: 0030 train_loss= 1.37767 train_acc= 0.30469 val_loss= 1.35694 val_acc= 0.36667 time= 0.01563
Epoch: 0031 train_loss= 1.37673 train_acc= 0.30469 val_loss= 1.35629 val_acc= 0.36667 time= 0.01563
Epoch: 0032 train_loss= 1.37723 train_acc= 0.30469 val_loss= 1.35586 val_acc= 0.36667 time= 0.01563
Epoch: 0033 train_loss= 1.37689 train_acc= 0.30469 val_loss= 1.35561 val_acc= 0.36667 time= 0.01562
Epoch: 0034 train_loss= 1.37728 train_acc= 0.30469 val_loss= 1.35540 val_acc= 0.36667 time= 0.01563
Epoch: 0035 train_loss= 1.37748 train_acc= 0.30469 val_loss= 1.35526 val_acc= 0.36667 time= 0.01563
Epoch: 0036 train_loss= 1.37717 train_acc= 0.30469 val_loss= 1.35553 val_acc= 0.36667 time= 0.01563
Epoch: 0037 train_loss= 1.37707 train_acc= 0.30469 val_loss= 1.35608 val_acc= 0.36667 time= 0.01563
Epoch: 0038 train_loss= 1.37685 train_acc= 0.30469 val_loss= 1.35669 val_acc= 0.36667 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38167 accuracy= 0.31667 time= 0.01563 
