Epoch: 0001 train_loss= 2.15606 train_acc= 0.12129 val_loss= 2.07414 val_acc= 0.10345 time= 0.98909
Epoch: 0002 train_loss= 2.11914 train_acc= 0.11590 val_loss= 2.06591 val_acc= 0.27586 time= 0.01563
Epoch: 0003 train_loss= 2.09737 train_acc= 0.16712 val_loss= 2.06917 val_acc= 0.17241 time= 0.01563
Epoch: 0004 train_loss= 2.07219 train_acc= 0.15903 val_loss= 2.08003 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.08731 train_acc= 0.13477 val_loss= 2.09241 val_acc= 0.13793 time= 0.02923
Epoch: 0006 train_loss= 2.06446 train_acc= 0.17251 val_loss= 2.10388 val_acc= 0.10345 time= 0.01500
Epoch: 0007 train_loss= 2.06373 train_acc= 0.17520 val_loss= 2.11760 val_acc= 0.10345 time= 0.01400
Epoch: 0008 train_loss= 2.05683 train_acc= 0.16981 val_loss= 2.12674 val_acc= 0.06897 time= 0.00806
Epoch: 0009 train_loss= 2.05766 train_acc= 0.16981 val_loss= 2.13069 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.05031 train_acc= 0.21563 val_loss= 2.13182 val_acc= 0.06897 time= 0.01562
Epoch: 0011 train_loss= 2.04622 train_acc= 0.15364 val_loss= 2.13407 val_acc= 0.06897 time= 0.01563
Epoch: 0012 train_loss= 2.04681 train_acc= 0.19946 val_loss= 2.13440 val_acc= 0.06897 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.09750 accuracy= 0.11864 time= 0.00000 
