Epoch: 0001 train_loss= 2.07665 train_acc= 0.12453 val_loss= 2.09598 val_acc= 0.06897 time= 0.52112
Epoch: 0002 train_loss= 2.07535 train_acc= 0.13585 val_loss= 2.09686 val_acc= 0.03448 time= 0.00500
Epoch: 0003 train_loss= 2.07206 train_acc= 0.16981 val_loss= 2.09780 val_acc= 0.03448 time= 0.00500
Epoch: 0004 train_loss= 2.06970 train_acc= 0.16981 val_loss= 2.09885 val_acc= 0.03448 time= 0.00400
Epoch: 0005 train_loss= 2.06732 train_acc= 0.18113 val_loss= 2.10010 val_acc= 0.03448 time= 0.00500
Epoch: 0006 train_loss= 2.06639 train_acc= 0.19245 val_loss= 2.10135 val_acc= 0.03448 time= 0.00500
Epoch: 0007 train_loss= 2.06306 train_acc= 0.18491 val_loss= 2.10265 val_acc= 0.03448 time= 0.00500
Epoch: 0008 train_loss= 2.06145 train_acc= 0.16981 val_loss= 2.10395 val_acc= 0.03448 time= 0.00500
Epoch: 0009 train_loss= 2.05982 train_acc= 0.18113 val_loss= 2.10522 val_acc= 0.03448 time= 0.00500
Epoch: 0010 train_loss= 2.05613 train_acc= 0.18113 val_loss= 2.10659 val_acc= 0.03448 time= 0.00500
Epoch: 0011 train_loss= 2.05441 train_acc= 0.18113 val_loss= 2.10803 val_acc= 0.03448 time= 0.00400
Epoch: 0012 train_loss= 2.05229 train_acc= 0.18113 val_loss= 2.10949 val_acc= 0.03448 time= 0.00500
Early stopping...
Optimization Finished!
Test set results: cost= 2.07389 accuracy= 0.13559 time= 0.00200 
