Epoch: 0001 train_loss= 2.06968 train_acc= 0.11950 val_loss= 2.05143 val_acc= 0.13793 time= 0.07813
Epoch: 0002 train_loss= 2.07419 train_acc= 0.13836 val_loss= 2.05492 val_acc= 0.13793 time= 0.01563
Epoch: 0003 train_loss= 2.08248 train_acc= 0.12579 val_loss= 2.05906 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.05157 train_acc= 0.20126 val_loss= 2.06330 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.04158 train_acc= 0.10692 val_loss= 2.06704 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.05715 train_acc= 0.18239 val_loss= 2.07117 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.04150 train_acc= 0.18868 val_loss= 2.07561 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.04140 train_acc= 0.22642 val_loss= 2.08004 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.03531 train_acc= 0.19497 val_loss= 2.08385 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.02833 train_acc= 0.19497 val_loss= 2.08793 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.02978 train_acc= 0.18868 val_loss= 2.09217 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.02169 train_acc= 0.20126 val_loss= 2.09623 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.03297 accuracy= 0.18644 time= 0.00000 
