Epoch: 0001 train_loss= 0.72083 train_acc= 0.52468 val_loss= 0.65231 val_acc= 0.60656 time= 1.00069
Epoch: 0002 train_loss= 0.71099 train_acc= 0.52597 val_loss= 0.65403 val_acc= 0.60656 time= 0.01563
Epoch: 0003 train_loss= 0.70358 train_acc= 0.52468 val_loss= 0.65605 val_acc= 0.60656 time= 0.00000
Epoch: 0004 train_loss= 0.70212 train_acc= 0.52468 val_loss= 0.65836 val_acc= 0.60656 time= 0.01563
Epoch: 0005 train_loss= 0.70448 train_acc= 0.52468 val_loss= 0.66092 val_acc= 0.60656 time= 0.00000
Epoch: 0006 train_loss= 0.69726 train_acc= 0.52078 val_loss= 0.66366 val_acc= 0.60656 time= 0.01563
Epoch: 0007 train_loss= 0.69851 train_acc= 0.52208 val_loss= 0.66659 val_acc= 0.60656 time= 0.00000
Epoch: 0008 train_loss= 0.69532 train_acc= 0.52208 val_loss= 0.66965 val_acc= 0.60656 time= 0.01563
Epoch: 0009 train_loss= 0.69542 train_acc= 0.52078 val_loss= 0.67284 val_acc= 0.60656 time= 0.01563
Epoch: 0010 train_loss= 0.69414 train_acc= 0.51429 val_loss= 0.67601 val_acc= 0.60656 time= 0.00000
Epoch: 0011 train_loss= 0.69312 train_acc= 0.52208 val_loss= 0.67910 val_acc= 0.60656 time= 0.01563
Epoch: 0012 train_loss= 0.69211 train_acc= 0.50260 val_loss= 0.68204 val_acc= 0.60656 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69840 accuracy= 0.47541 time= 0.00000 
