Epoch: 0001 train_loss= 1.39373 train_acc= 0.17590 val_loss= 1.39308 val_acc= 0.17857 time= 0.11176
Epoch: 0002 train_loss= 1.39172 train_acc= 0.34528 val_loss= 1.39372 val_acc= 0.17857 time= 0.01563
Epoch: 0003 train_loss= 1.39033 train_acc= 0.33876 val_loss= 1.39439 val_acc= 0.17857 time= 0.01563
Epoch: 0004 train_loss= 1.38865 train_acc= 0.34202 val_loss= 1.39515 val_acc= 0.17857 time= 0.01563
Epoch: 0005 train_loss= 1.38728 train_acc= 0.34202 val_loss= 1.39598 val_acc= 0.17857 time= 0.01563
Epoch: 0006 train_loss= 1.38577 train_acc= 0.34202 val_loss= 1.39688 val_acc= 0.17857 time= 0.01563
Epoch: 0007 train_loss= 1.38443 train_acc= 0.34202 val_loss= 1.39755 val_acc= 0.17857 time= 0.01563
Epoch: 0008 train_loss= 1.38288 train_acc= 0.34202 val_loss= 1.39826 val_acc= 0.17857 time= 0.01563
Epoch: 0009 train_loss= 1.38150 train_acc= 0.34202 val_loss= 1.39905 val_acc= 0.17857 time= 0.01562
Epoch: 0010 train_loss= 1.38000 train_acc= 0.34202 val_loss= 1.39994 val_acc= 0.17857 time= 0.00000
Epoch: 0011 train_loss= 1.37819 train_acc= 0.34202 val_loss= 1.40094 val_acc= 0.17857 time= 0.01563
Epoch: 0012 train_loss= 1.37630 train_acc= 0.34202 val_loss= 1.40208 val_acc= 0.17857 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37967 accuracy= 0.29204 time= 0.01563 
