Epoch: 0001 train_loss= 1.39333 train_acc= 0.25407 val_loss= 1.39442 val_acc= 0.16071 time= 0.10938
Epoch: 0002 train_loss= 1.39209 train_acc= 0.26384 val_loss= 1.39358 val_acc= 0.16071 time= 0.01563
Epoch: 0003 train_loss= 1.39093 train_acc= 0.26059 val_loss= 1.39324 val_acc= 0.16071 time= 0.01563
Epoch: 0004 train_loss= 1.39009 train_acc= 0.28664 val_loss= 1.39325 val_acc= 0.28571 time= 0.00000
Epoch: 0005 train_loss= 1.38960 train_acc= 0.29642 val_loss= 1.39366 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38894 train_acc= 0.27362 val_loss= 1.39407 val_acc= 0.28571 time= 0.01562
Epoch: 0007 train_loss= 1.38809 train_acc= 0.28990 val_loss= 1.39456 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38758 train_acc= 0.27687 val_loss= 1.39507 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.38621 train_acc= 0.27687 val_loss= 1.39571 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.38614 train_acc= 0.28990 val_loss= 1.39629 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.38564 train_acc= 0.29316 val_loss= 1.39672 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38507 train_acc= 0.28664 val_loss= 1.39698 val_acc= 0.28571 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.36825 accuracy= 0.36283 time= 0.00000 
