Epoch: 0001 train_loss= 1.39412 train_acc= 0.23127 val_loss= 1.39234 val_acc= 0.21429 time= 0.23459
Epoch: 0002 train_loss= 1.39030 train_acc= 0.37134 val_loss= 1.39146 val_acc= 0.21429 time= 0.01562
Epoch: 0003 train_loss= 1.38617 train_acc= 0.37134 val_loss= 1.39147 val_acc= 0.21429 time= 0.01563
Epoch: 0004 train_loss= 1.38273 train_acc= 0.37134 val_loss= 1.39225 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.37927 train_acc= 0.37134 val_loss= 1.39374 val_acc= 0.21429 time= 0.01563
Epoch: 0006 train_loss= 1.37621 train_acc= 0.37134 val_loss= 1.39585 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.37341 train_acc= 0.37134 val_loss= 1.39848 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.37224 train_acc= 0.37134 val_loss= 1.40149 val_acc= 0.21429 time= 0.01563
Epoch: 0009 train_loss= 1.36822 train_acc= 0.37134 val_loss= 1.40491 val_acc= 0.21429 time= 0.01562
Epoch: 0010 train_loss= 1.36819 train_acc= 0.37134 val_loss= 1.40855 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.36524 train_acc= 0.37134 val_loss= 1.41242 val_acc= 0.21429 time= 0.01563
Epoch: 0012 train_loss= 1.36449 train_acc= 0.37134 val_loss= 1.41640 val_acc= 0.21429 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.38546 accuracy= 0.29204 time= 0.00000 
