Epoch: 0001 train_loss= 2.08870 train_acc= 0.11321 val_loss= 2.08320 val_acc= 0.13793 time= 0.20341
Epoch: 0002 train_loss= 2.08116 train_acc= 0.10063 val_loss= 2.08167 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.07798 train_acc= 0.16981 val_loss= 2.08075 val_acc= 0.13793 time= 0.00000
Epoch: 0004 train_loss= 2.07573 train_acc= 0.16981 val_loss= 2.08026 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07204 train_acc= 0.17610 val_loss= 2.07991 val_acc= 0.13793 time= 0.01562
Epoch: 0006 train_loss= 2.06678 train_acc= 0.18868 val_loss= 2.08000 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.05908 train_acc= 0.19497 val_loss= 2.08060 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.06140 train_acc= 0.18868 val_loss= 2.08168 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.05525 train_acc= 0.18868 val_loss= 2.08331 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.05475 train_acc= 0.18868 val_loss= 2.08529 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.05578 train_acc= 0.18868 val_loss= 2.08797 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.04931 train_acc= 0.18239 val_loss= 2.09116 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07764 accuracy= 0.10169 time= 0.00000 
