Epoch: 0001 train_loss= 1.41124 train_acc= 0.24104 val_loss= 1.40511 val_acc= 0.17857 time= 0.07860
Epoch: 0002 train_loss= 1.40849 train_acc= 0.21498 val_loss= 1.39663 val_acc= 0.21429 time= 0.01515
Epoch: 0003 train_loss= 1.40264 train_acc= 0.21498 val_loss= 1.39240 val_acc= 0.19643 time= 0.01563
Epoch: 0004 train_loss= 1.39142 train_acc= 0.27362 val_loss= 1.38927 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.38910 train_acc= 0.27687 val_loss= 1.38834 val_acc= 0.25000 time= 0.01563
Epoch: 0006 train_loss= 1.37739 train_acc= 0.32248 val_loss= 1.38698 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.38344 train_acc= 0.29642 val_loss= 1.38844 val_acc= 0.25000 time= 0.01563
Epoch: 0008 train_loss= 1.38630 train_acc= 0.28013 val_loss= 1.38981 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.38411 train_acc= 0.28339 val_loss= 1.38765 val_acc= 0.23214 time= 0.01563
Epoch: 0010 train_loss= 1.37917 train_acc= 0.31922 val_loss= 1.38616 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.37901 train_acc= 0.30945 val_loss= 1.38600 val_acc= 0.23214 time= 0.01562
Epoch: 0012 train_loss= 1.39080 train_acc= 0.30945 val_loss= 1.38408 val_acc= 0.19643 time= 0.01563
Epoch: 0013 train_loss= 1.38166 train_acc= 0.31270 val_loss= 1.37987 val_acc= 0.23214 time= 0.00000
Epoch: 0014 train_loss= 1.38468 train_acc= 0.31270 val_loss= 1.37470 val_acc= 0.25000 time= 0.00000
Epoch: 0015 train_loss= 1.38115 train_acc= 0.29642 val_loss= 1.37199 val_acc= 0.23214 time= 0.01563
Epoch: 0016 train_loss= 1.37587 train_acc= 0.32899 val_loss= 1.36846 val_acc= 0.26786 time= 0.01563
Epoch: 0017 train_loss= 1.36855 train_acc= 0.32248 val_loss= 1.36549 val_acc= 0.25000 time= 0.01563
Epoch: 0018 train_loss= 1.38114 train_acc= 0.26384 val_loss= 1.36341 val_acc= 0.28571 time= 0.01563
Epoch: 0019 train_loss= 1.37229 train_acc= 0.31922 val_loss= 1.36152 val_acc= 0.32143 time= 0.01563
Epoch: 0020 train_loss= 1.37830 train_acc= 0.29316 val_loss= 1.35974 val_acc= 0.35714 time= 0.01563
Epoch: 0021 train_loss= 1.37191 train_acc= 0.29642 val_loss= 1.35844 val_acc= 0.35714 time= 0.01563
Epoch: 0022 train_loss= 1.36603 train_acc= 0.33876 val_loss= 1.35722 val_acc= 0.35714 time= 0.01563
Epoch: 0023 train_loss= 1.37626 train_acc= 0.29316 val_loss= 1.35801 val_acc= 0.35714 time= 0.01563
Epoch: 0024 train_loss= 1.36706 train_acc= 0.31922 val_loss= 1.35929 val_acc= 0.32143 time= 0.01563
Epoch: 0025 train_loss= 1.36904 train_acc= 0.29316 val_loss= 1.36202 val_acc= 0.28571 time= 0.01563
Epoch: 0026 train_loss= 1.36836 train_acc= 0.32899 val_loss= 1.36522 val_acc= 0.26786 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39945 accuracy= 0.30088 time= 0.00000 
