Epoch: 0001 train_loss= 2.08982 train_acc= 0.08176 val_loss= 2.09061 val_acc= 0.03448 time= 0.21876
Epoch: 0002 train_loss= 2.08681 train_acc= 0.09434 val_loss= 2.09233 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08499 train_acc= 0.16981 val_loss= 2.09445 val_acc= 0.13793 time= 0.01563
Epoch: 0004 train_loss= 2.08292 train_acc= 0.17610 val_loss= 2.09678 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07778 train_acc= 0.17610 val_loss= 2.09924 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.07623 train_acc= 0.17610 val_loss= 2.10184 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07359 train_acc= 0.17610 val_loss= 2.10456 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07305 train_acc= 0.17610 val_loss= 2.10737 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.07109 train_acc= 0.17610 val_loss= 2.11023 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06914 train_acc= 0.17610 val_loss= 2.11310 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.06507 train_acc= 0.17610 val_loss= 2.11611 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.06203 train_acc= 0.17610 val_loss= 2.11915 val_acc= 0.13793 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 2.08935 accuracy= 0.11864 time= 0.00000 
