Epoch: 0001 train_loss= 1.39279 train_acc= 0.24430 val_loss= 1.38090 val_acc= 0.39286 time= 0.09376
Epoch: 0002 train_loss= 1.38216 train_acc= 0.29967 val_loss= 1.37314 val_acc= 0.39286 time= 0.01563
Epoch: 0003 train_loss= 1.37707 train_acc= 0.30619 val_loss= 1.36669 val_acc= 0.37500 time= 0.00000
Epoch: 0004 train_loss= 1.37790 train_acc= 0.30619 val_loss= 1.36074 val_acc= 0.39286 time= 0.01563
Epoch: 0005 train_loss= 1.37644 train_acc= 0.30293 val_loss= 1.35630 val_acc= 0.41071 time= 0.01563
Epoch: 0006 train_loss= 1.37168 train_acc= 0.29967 val_loss= 1.35256 val_acc= 0.41071 time= 0.01562
Epoch: 0007 train_loss= 1.37696 train_acc= 0.30293 val_loss= 1.34991 val_acc= 0.41071 time= 0.01563
Epoch: 0008 train_loss= 1.37741 train_acc= 0.30293 val_loss= 1.34930 val_acc= 0.41071 time= 0.01563
Epoch: 0009 train_loss= 1.38182 train_acc= 0.28013 val_loss= 1.34909 val_acc= 0.41071 time= 0.01563
Epoch: 0010 train_loss= 1.37491 train_acc= 0.31596 val_loss= 1.34959 val_acc= 0.41071 time= 0.01563
Epoch: 0011 train_loss= 1.38021 train_acc= 0.30619 val_loss= 1.35089 val_acc= 0.41071 time= 0.01562
Epoch: 0012 train_loss= 1.37229 train_acc= 0.29967 val_loss= 1.35192 val_acc= 0.41071 time= 0.01563
Epoch: 0013 train_loss= 1.37740 train_acc= 0.30619 val_loss= 1.35330 val_acc= 0.41071 time= 0.01563
Epoch: 0014 train_loss= 1.36913 train_acc= 0.30619 val_loss= 1.35440 val_acc= 0.41071 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39342 accuracy= 0.30973 time= 0.00000 
