Epoch: 0001 train_loss= 1.39005 train_acc= 0.24162 val_loss= 1.39787 val_acc= 0.16071 time= 0.87506
Epoch: 0002 train_loss= 1.38983 train_acc= 0.24162 val_loss= 1.39501 val_acc= 0.16071 time= 0.01563
Epoch: 0003 train_loss= 1.38799 train_acc= 0.24581 val_loss= 1.39226 val_acc= 0.16071 time= 0.00000
Epoch: 0004 train_loss= 1.38727 train_acc= 0.25140 val_loss= 1.38958 val_acc= 0.16071 time= 0.01563
Epoch: 0005 train_loss= 1.38607 train_acc= 0.25000 val_loss= 1.38695 val_acc= 0.17857 time= 0.00000
Epoch: 0006 train_loss= 1.38407 train_acc= 0.29190 val_loss= 1.38435 val_acc= 0.35714 time= 0.01563
Epoch: 0007 train_loss= 1.38435 train_acc= 0.28911 val_loss= 1.38178 val_acc= 0.39286 time= 0.00000
Epoch: 0008 train_loss= 1.38289 train_acc= 0.29888 val_loss= 1.37919 val_acc= 0.39286 time= 0.00000
Epoch: 0009 train_loss= 1.38226 train_acc= 0.30587 val_loss= 1.37664 val_acc= 0.39286 time= 0.01563
Epoch: 0010 train_loss= 1.38313 train_acc= 0.30028 val_loss= 1.37411 val_acc= 0.39286 time= 0.00000
Epoch: 0011 train_loss= 1.38114 train_acc= 0.30587 val_loss= 1.37159 val_acc= 0.39286 time= 0.01563
Epoch: 0012 train_loss= 1.38130 train_acc= 0.30587 val_loss= 1.36908 val_acc= 0.39286 time= 0.00000
Epoch: 0013 train_loss= 1.38101 train_acc= 0.30307 val_loss= 1.36658 val_acc= 0.39286 time= 0.01563
Epoch: 0014 train_loss= 1.38085 train_acc= 0.30447 val_loss= 1.36412 val_acc= 0.39286 time= 0.00000
Epoch: 0015 train_loss= 1.37935 train_acc= 0.30587 val_loss= 1.36169 val_acc= 0.39286 time= 0.01563
Epoch: 0016 train_loss= 1.37897 train_acc= 0.30587 val_loss= 1.35935 val_acc= 0.39286 time= 0.00000
Epoch: 0017 train_loss= 1.37995 train_acc= 0.30447 val_loss= 1.35736 val_acc= 0.39286 time= 0.01563
Epoch: 0018 train_loss= 1.37890 train_acc= 0.30587 val_loss= 1.35578 val_acc= 0.39286 time= 0.00000
Epoch: 0019 train_loss= 1.37927 train_acc= 0.30587 val_loss= 1.35457 val_acc= 0.39286 time= 0.01563
Epoch: 0020 train_loss= 1.37900 train_acc= 0.30587 val_loss= 1.35358 val_acc= 0.39286 time= 0.00000
Epoch: 0021 train_loss= 1.37875 train_acc= 0.30587 val_loss= 1.35288 val_acc= 0.39286 time= 0.01563
Epoch: 0022 train_loss= 1.37853 train_acc= 0.30587 val_loss= 1.35252 val_acc= 0.39286 time= 0.00000
Epoch: 0023 train_loss= 1.37889 train_acc= 0.30587 val_loss= 1.35244 val_acc= 0.39286 time= 0.00000
Epoch: 0024 train_loss= 1.37962 train_acc= 0.30587 val_loss= 1.35256 val_acc= 0.39286 time= 0.01563
Epoch: 0025 train_loss= 1.37935 train_acc= 0.30447 val_loss= 1.35286 val_acc= 0.39286 time= 0.00000
Epoch: 0026 train_loss= 1.38041 train_acc= 0.30307 val_loss= 1.35322 val_acc= 0.39286 time= 0.01562
Epoch: 0027 train_loss= 1.37970 train_acc= 0.30587 val_loss= 1.35365 val_acc= 0.39286 time= 0.00000
Epoch: 0028 train_loss= 1.37789 train_acc= 0.30587 val_loss= 1.35407 val_acc= 0.39286 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37355 accuracy= 0.29204 time= 0.00000 
