Epoch: 0001 train_loss= 1.39425 train_acc= 0.20508 val_loss= 1.39151 val_acc= 0.21429 time= 0.23439
Epoch: 0002 train_loss= 1.39117 train_acc= 0.31055 val_loss= 1.38935 val_acc= 0.21429 time= 0.01563
Epoch: 0003 train_loss= 1.38846 train_acc= 0.31055 val_loss= 1.38756 val_acc= 0.21429 time= 0.01562
Epoch: 0004 train_loss= 1.38605 train_acc= 0.31055 val_loss= 1.38620 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.38447 train_acc= 0.31055 val_loss= 1.38525 val_acc= 0.21429 time= 0.01563
Epoch: 0006 train_loss= 1.38305 train_acc= 0.31055 val_loss= 1.38475 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.38216 train_acc= 0.31055 val_loss= 1.38452 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.38101 train_acc= 0.31055 val_loss= 1.38462 val_acc= 0.21429 time= 0.01563
Epoch: 0009 train_loss= 1.38045 train_acc= 0.31055 val_loss= 1.38490 val_acc= 0.21429 time= 0.01563
Epoch: 0010 train_loss= 1.37988 train_acc= 0.31055 val_loss= 1.38520 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.37971 train_acc= 0.31055 val_loss= 1.38543 val_acc= 0.21429 time= 0.01563
Epoch: 0012 train_loss= 1.37926 train_acc= 0.31055 val_loss= 1.38557 val_acc= 0.21429 time= 0.01563
Epoch: 0013 train_loss= 1.37961 train_acc= 0.31055 val_loss= 1.38548 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38522 accuracy= 0.30088 time= 0.00000 
