Epoch: 0001 train_loss= 2.08727 train_acc= 0.11321 val_loss= 2.08584 val_acc= 0.10345 time= 0.45343
Epoch: 0002 train_loss= 2.08491 train_acc= 0.14340 val_loss= 2.08443 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08290 train_acc= 0.13208 val_loss= 2.08364 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08124 train_acc= 0.12830 val_loss= 2.08319 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.07986 train_acc= 0.13208 val_loss= 2.08297 val_acc= 0.10345 time= 0.00000
Epoch: 0006 train_loss= 2.07832 train_acc= 0.12453 val_loss= 2.08289 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.07758 train_acc= 0.14717 val_loss= 2.08316 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.07661 train_acc= 0.15472 val_loss= 2.08344 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07521 train_acc= 0.15094 val_loss= 2.08377 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.07364 train_acc= 0.18868 val_loss= 2.08433 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.07195 train_acc= 0.18113 val_loss= 2.08503 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.07085 train_acc= 0.15472 val_loss= 2.08579 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08498 accuracy= 0.20339 time= 0.01563 
