Epoch: 0001 train_loss= 1.39410 train_acc= 0.27362 val_loss= 1.39118 val_acc= 0.28571 time= 0.16964
Epoch: 0002 train_loss= 1.39076 train_acc= 0.31596 val_loss= 1.38886 val_acc= 0.28571 time= 0.01563
Epoch: 0003 train_loss= 1.38797 train_acc= 0.31596 val_loss= 1.38718 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.38548 train_acc= 0.31596 val_loss= 1.38608 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38364 train_acc= 0.31596 val_loss= 1.38558 val_acc= 0.28571 time= 0.01562
Epoch: 0006 train_loss= 1.38193 train_acc= 0.31596 val_loss= 1.38559 val_acc= 0.28571 time= 0.00000
Epoch: 0007 train_loss= 1.38145 train_acc= 0.31596 val_loss= 1.38599 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38060 train_acc= 0.31596 val_loss= 1.38656 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.37962 train_acc= 0.31596 val_loss= 1.38720 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.37931 train_acc= 0.31596 val_loss= 1.38785 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.37937 train_acc= 0.31596 val_loss= 1.38853 val_acc= 0.28571 time= 0.00000
Epoch: 0012 train_loss= 1.37874 train_acc= 0.31596 val_loss= 1.38912 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38401 accuracy= 0.29204 time= 0.00000 
