Epoch: 0001 train_loss= 1.39828 train_acc= 0.31006 val_loss= 1.38781 val_acc= 0.23214 time= 0.31252
Epoch: 0002 train_loss= 1.39289 train_acc= 0.30866 val_loss= 1.38915 val_acc= 0.23214 time= 0.01563
Epoch: 0003 train_loss= 1.39914 train_acc= 0.26955 val_loss= 1.39085 val_acc= 0.25000 time= 0.03125
Epoch: 0004 train_loss= 1.38693 train_acc= 0.31006 val_loss= 1.39273 val_acc= 0.25000 time= 0.01563
Epoch: 0005 train_loss= 1.38533 train_acc= 0.30307 val_loss= 1.39455 val_acc= 0.25000 time= 0.01563
Epoch: 0006 train_loss= 1.38683 train_acc= 0.30866 val_loss= 1.39694 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.38587 train_acc= 0.28911 val_loss= 1.39903 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.38300 train_acc= 0.31704 val_loss= 1.40130 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.38616 train_acc= 0.31006 val_loss= 1.40376 val_acc= 0.26786 time= 0.01563
Epoch: 0010 train_loss= 1.38069 train_acc= 0.30307 val_loss= 1.40574 val_acc= 0.26786 time= 0.03125
Epoch: 0011 train_loss= 1.38307 train_acc= 0.31006 val_loss= 1.40731 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.38657 train_acc= 0.31844 val_loss= 1.40821 val_acc= 0.26786 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.35651 accuracy= 0.36283 time= 0.00000 
