Epoch: 0001 train_loss= 1.39397 train_acc= 0.32682 val_loss= 1.39253 val_acc= 0.23214 time= 0.78143
Epoch: 0002 train_loss= 1.39034 train_acc= 0.32682 val_loss= 1.39186 val_acc= 0.23214 time= 0.01562
Epoch: 0003 train_loss= 1.38714 train_acc= 0.32682 val_loss= 1.39215 val_acc= 0.23214 time= 0.01562
Epoch: 0004 train_loss= 1.38464 train_acc= 0.32682 val_loss= 1.39320 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.38232 train_acc= 0.32682 val_loss= 1.39486 val_acc= 0.23214 time= 0.00000
Epoch: 0006 train_loss= 1.38017 train_acc= 0.32682 val_loss= 1.39708 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.37978 train_acc= 0.32682 val_loss= 1.39960 val_acc= 0.23214 time= 0.01563
Epoch: 0008 train_loss= 1.37849 train_acc= 0.32682 val_loss= 1.40230 val_acc= 0.23214 time= 0.01563
Epoch: 0009 train_loss= 1.37793 train_acc= 0.32682 val_loss= 1.40497 val_acc= 0.23214 time= 0.01562
Epoch: 0010 train_loss= 1.37712 train_acc= 0.32682 val_loss= 1.40750 val_acc= 0.23214 time= 0.00000
Epoch: 0011 train_loss= 1.37631 train_acc= 0.32682 val_loss= 1.40983 val_acc= 0.23214 time= 0.01563
Epoch: 0012 train_loss= 1.37616 train_acc= 0.32682 val_loss= 1.41185 val_acc= 0.23214 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.38234 accuracy= 0.29204 time= 0.00000 
