Epoch: 0001 train_loss= 1.39158 train_acc= 0.23633 val_loss= 1.38958 val_acc= 0.28571 time= 0.25002
Epoch: 0002 train_loss= 1.38990 train_acc= 0.29883 val_loss= 1.38911 val_acc= 0.28571 time= 0.01563
Epoch: 0003 train_loss= 1.38823 train_acc= 0.29688 val_loss= 1.38851 val_acc= 0.28571 time= 0.01562
Epoch: 0004 train_loss= 1.38754 train_acc= 0.29883 val_loss= 1.38815 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38647 train_acc= 0.29883 val_loss= 1.38816 val_acc= 0.28571 time= 0.03125
Epoch: 0006 train_loss= 1.38566 train_acc= 0.29883 val_loss= 1.38839 val_acc= 0.28571 time= 0.01563
Epoch: 0007 train_loss= 1.38452 train_acc= 0.29883 val_loss= 1.38879 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38425 train_acc= 0.29883 val_loss= 1.38938 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.38305 train_acc= 0.29883 val_loss= 1.39010 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.38289 train_acc= 0.29883 val_loss= 1.39101 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.38242 train_acc= 0.29883 val_loss= 1.39195 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38295 train_acc= 0.29883 val_loss= 1.39275 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37463 accuracy= 0.30973 time= 0.00000 
