Epoch: 0001 train_loss= 2.02206 train_acc= 0.21824 val_loss= 1.66763 val_acc= 0.35714 time= 0.36658
Epoch: 0002 train_loss= 2.12971 train_acc= 0.25407 val_loss= 1.58100 val_acc= 0.35714 time= 0.03148
Epoch: 0003 train_loss= 3.64366 train_acc= 0.30293 val_loss= 1.39364 val_acc= 0.35714 time= 0.02701
Epoch: 0004 train_loss= 2.06629 train_acc= 0.29967 val_loss= 1.44755 val_acc= 0.28571 time= 0.02200
Epoch: 0005 train_loss= 1.74988 train_acc= 0.30293 val_loss= 1.53335 val_acc= 0.25000 time= 0.02119
Epoch: 0006 train_loss= 1.63265 train_acc= 0.26710 val_loss= 1.55115 val_acc= 0.23214 time= 0.01924
Epoch: 0007 train_loss= 1.45465 train_acc= 0.25407 val_loss= 1.57874 val_acc= 0.23214 time= 0.02024
Epoch: 0008 train_loss= 1.63015 train_acc= 0.28664 val_loss= 1.55882 val_acc= 0.21429 time= 0.02006
Epoch: 0009 train_loss= 1.69278 train_acc= 0.23779 val_loss= 1.52905 val_acc= 0.21429 time= 0.02000
Epoch: 0010 train_loss= 1.44464 train_acc= 0.28339 val_loss= 1.49516 val_acc= 0.23214 time= 0.01458
Epoch: 0011 train_loss= 1.45066 train_acc= 0.28664 val_loss= 1.46079 val_acc= 0.23214 time= 0.02685
Epoch: 0012 train_loss= 1.52803 train_acc= 0.23453 val_loss= 1.42980 val_acc= 0.19643 time= 0.02000
Epoch: 0013 train_loss= 1.42016 train_acc= 0.27362 val_loss= 1.41095 val_acc= 0.21429 time= 0.02079
Epoch: 0014 train_loss= 1.46644 train_acc= 0.23779 val_loss= 1.39884 val_acc= 0.28571 time= 0.02426
Epoch: 0015 train_loss= 1.66981 train_acc= 0.31922 val_loss= 1.39829 val_acc= 0.30357 time= 0.03116
Epoch: 0016 train_loss= 1.37453 train_acc= 0.28990 val_loss= 1.39819 val_acc= 0.30357 time= 0.03001
Epoch: 0017 train_loss= 1.39391 train_acc= 0.30293 val_loss= 1.39849 val_acc= 0.28571 time= 0.02274
Epoch: 0018 train_loss= 1.86126 train_acc= 0.26059 val_loss= 1.39927 val_acc= 0.28571 time= 0.02515
Epoch: 0019 train_loss= 1.39213 train_acc= 0.29967 val_loss= 1.39982 val_acc= 0.28571 time= 0.02135
Epoch: 0020 train_loss= 1.39392 train_acc= 0.28339 val_loss= 1.40060 val_acc= 0.28571 time= 0.02364
Epoch: 0021 train_loss= 1.39123 train_acc= 0.29967 val_loss= 1.40075 val_acc= 0.28571 time= 0.02112
Epoch: 0022 train_loss= 1.40427 train_acc= 0.28339 val_loss= 1.40056 val_acc= 0.28571 time= 0.02020
Epoch: 0023 train_loss= 1.38870 train_acc= 0.25407 val_loss= 1.40064 val_acc= 0.28571 time= 0.02116
Early stopping...
Optimization Finished!
Test set results: cost= 1.37134 accuracy= 0.36283 time= 0.00900 
