Epoch: 0001 train_loss= 2.11653 train_acc= 0.11321 val_loss= 2.12663 val_acc= 0.13793 time= 0.31656
Epoch: 0002 train_loss= 2.10373 train_acc= 0.15723 val_loss= 2.12847 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.09405 train_acc= 0.16352 val_loss= 2.12898 val_acc= 0.17241 time= 0.01562
Epoch: 0004 train_loss= 2.07728 train_acc= 0.16352 val_loss= 2.13588 val_acc= 0.24138 time= 0.01563
Epoch: 0005 train_loss= 2.07962 train_acc= 0.13208 val_loss= 2.14347 val_acc= 0.24138 time= 0.01563
Epoch: 0006 train_loss= 2.05913 train_acc= 0.18239 val_loss= 2.15272 val_acc= 0.20690 time= 0.01563
Epoch: 0007 train_loss= 2.03881 train_acc= 0.20755 val_loss= 2.16095 val_acc= 0.20690 time= 0.01563
Epoch: 0008 train_loss= 2.02986 train_acc= 0.20755 val_loss= 2.16759 val_acc= 0.24138 time= 0.00000
Epoch: 0009 train_loss= 2.05407 train_acc= 0.18868 val_loss= 2.17176 val_acc= 0.24138 time= 0.01563
Epoch: 0010 train_loss= 2.03802 train_acc= 0.20755 val_loss= 2.17543 val_acc= 0.20690 time= 0.01563
Epoch: 0011 train_loss= 2.06429 train_acc= 0.21384 val_loss= 2.17944 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.02988 train_acc= 0.22642 val_loss= 2.18667 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.10580 accuracy= 0.11864 time= 0.00000 
