Epoch: 0001 train_loss= 1.39422 train_acc= 0.32031 val_loss= 1.39034 val_acc= 0.33929 time= 0.56305
Epoch: 0002 train_loss= 1.39086 train_acc= 0.32227 val_loss= 1.38740 val_acc= 0.33929 time= 0.01563
Epoch: 0003 train_loss= 1.38810 train_acc= 0.32227 val_loss= 1.38529 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38565 train_acc= 0.32227 val_loss= 1.38417 val_acc= 0.33929 time= 0.01563
Epoch: 0005 train_loss= 1.38399 train_acc= 0.32227 val_loss= 1.38382 val_acc= 0.33929 time= 0.01562
Epoch: 0006 train_loss= 1.38275 train_acc= 0.32227 val_loss= 1.38426 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.38131 train_acc= 0.32227 val_loss= 1.38528 val_acc= 0.33929 time= 0.01563
Epoch: 0008 train_loss= 1.38085 train_acc= 0.32227 val_loss= 1.38660 val_acc= 0.33929 time= 0.01563
Epoch: 0009 train_loss= 1.37951 train_acc= 0.32227 val_loss= 1.38808 val_acc= 0.33929 time= 0.01562
Epoch: 0010 train_loss= 1.37941 train_acc= 0.32227 val_loss= 1.38969 val_acc= 0.33929 time= 0.01563
Epoch: 0011 train_loss= 1.37891 train_acc= 0.32227 val_loss= 1.39136 val_acc= 0.33929 time= 0.01563
Epoch: 0012 train_loss= 1.37902 train_acc= 0.32227 val_loss= 1.39305 val_acc= 0.33929 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37956 accuracy= 0.30973 time= 0.01563 
