Epoch: 0001 train_loss= 2.12647 train_acc= 0.09434 val_loss= 2.10170 val_acc= 0.10345 time= 0.29716
Epoch: 0002 train_loss= 2.09347 train_acc= 0.11321 val_loss= 2.09150 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.09391 train_acc= 0.11950 val_loss= 2.08717 val_acc= 0.17241 time= 0.01563
Epoch: 0004 train_loss= 2.06747 train_acc= 0.15723 val_loss= 2.08803 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.05160 train_acc= 0.16981 val_loss= 2.09071 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.04989 train_acc= 0.19497 val_loss= 2.09321 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.05088 train_acc= 0.17610 val_loss= 2.09386 val_acc= 0.13793 time= 0.01563
Epoch: 0008 train_loss= 2.03226 train_acc= 0.20126 val_loss= 2.09256 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.04916 train_acc= 0.19497 val_loss= 2.08804 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.01968 train_acc= 0.20126 val_loss= 2.08434 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.03614 train_acc= 0.18239 val_loss= 2.08194 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.03102 train_acc= 0.20126 val_loss= 2.07951 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.04094 train_acc= 0.22013 val_loss= 2.07768 val_acc= 0.06897 time= 0.01563
Epoch: 0014 train_loss= 2.01781 train_acc= 0.18868 val_loss= 2.07726 val_acc= 0.06897 time= 0.01563
Epoch: 0015 train_loss= 2.01467 train_acc= 0.19497 val_loss= 2.07696 val_acc= 0.06897 time= 0.01563
Epoch: 0016 train_loss= 2.02312 train_acc= 0.19497 val_loss= 2.07798 val_acc= 0.17241 time= 0.00000
Epoch: 0017 train_loss= 2.02751 train_acc= 0.18239 val_loss= 2.07963 val_acc= 0.20690 time= 0.01563
Epoch: 0018 train_loss= 2.02419 train_acc= 0.18239 val_loss= 2.08293 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.13669 accuracy= 0.16949 time= 0.00000 
