Epoch: 0001 train_loss= 1.68569 train_acc= 0.19870 val_loss= 1.76567 val_acc= 0.25000 time= 0.28602
Epoch: 0002 train_loss= 2.31992 train_acc= 0.23127 val_loss= 1.48859 val_acc= 0.37500 time= 0.02663
Epoch: 0003 train_loss= 1.78995 train_acc= 0.29642 val_loss= 1.45603 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 2.74264 train_acc= 0.21824 val_loss= 1.45520 val_acc= 0.37500 time= 0.01563
Epoch: 0005 train_loss= 1.98238 train_acc= 0.24756 val_loss= 1.49935 val_acc= 0.32143 time= 0.03125
Epoch: 0006 train_loss= 1.56221 train_acc= 0.30945 val_loss= 1.50701 val_acc= 0.32143 time= 0.03125
Epoch: 0007 train_loss= 1.61983 train_acc= 0.31270 val_loss= 1.49269 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.56378 train_acc= 0.31596 val_loss= 1.47393 val_acc= 0.30357 time= 0.01563
Epoch: 0009 train_loss= 1.45453 train_acc= 0.29316 val_loss= 1.47543 val_acc= 0.28571 time= 0.03125
Epoch: 0010 train_loss= 1.42911 train_acc= 0.27362 val_loss= 1.48411 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 2.14407 train_acc= 0.27036 val_loss= 1.43488 val_acc= 0.30357 time= 0.03125
Epoch: 0012 train_loss= 1.48140 train_acc= 0.28013 val_loss= 1.38720 val_acc= 0.28571 time= 0.01563
Epoch: 0013 train_loss= 1.44616 train_acc= 0.25081 val_loss= 1.37525 val_acc= 0.35714 time= 0.01563
Epoch: 0014 train_loss= 1.99735 train_acc= 0.25081 val_loss= 1.37153 val_acc= 0.33929 time= 0.03125
Epoch: 0015 train_loss= 1.41204 train_acc= 0.27036 val_loss= 1.37299 val_acc= 0.32143 time= 0.03125
Epoch: 0016 train_loss= 1.43210 train_acc= 0.25733 val_loss= 1.37186 val_acc= 0.30357 time= 0.01563
Epoch: 0017 train_loss= 1.40297 train_acc= 0.31596 val_loss= 1.37104 val_acc= 0.33929 time= 0.03125
Epoch: 0018 train_loss= 1.37392 train_acc= 0.30293 val_loss= 1.37044 val_acc= 0.35714 time= 0.01563
Epoch: 0019 train_loss= 1.38952 train_acc= 0.28339 val_loss= 1.37015 val_acc= 0.37500 time= 0.01563
Epoch: 0020 train_loss= 1.38254 train_acc= 0.31270 val_loss= 1.37005 val_acc= 0.39286 time= 0.03125
Epoch: 0021 train_loss= 1.40140 train_acc= 0.28013 val_loss= 1.37017 val_acc= 0.37500 time= 0.01563
Epoch: 0022 train_loss= 1.37558 train_acc= 0.33550 val_loss= 1.37035 val_acc= 0.37500 time= 0.03125
Epoch: 0023 train_loss= 1.37585 train_acc= 0.30945 val_loss= 1.37062 val_acc= 0.39286 time= 0.03125
Epoch: 0024 train_loss= 1.38572 train_acc= 0.28990 val_loss= 1.37076 val_acc= 0.39286 time= 0.01563
Epoch: 0025 train_loss= 1.37510 train_acc= 0.29967 val_loss= 1.37095 val_acc= 0.39286 time= 0.03125
Early stopping...
Optimization Finished!
Test set results: cost= 1.40117 accuracy= 0.30973 time= 0.01562 
