Epoch: 0001 train_loss= 2.09184 train_acc= 0.12075 val_loss= 2.05643 val_acc= 0.17241 time= 0.40009
Epoch: 0002 train_loss= 2.08851 train_acc= 0.12830 val_loss= 2.05882 val_acc= 0.17241 time= 0.00800
Epoch: 0003 train_loss= 2.08387 train_acc= 0.13585 val_loss= 2.06243 val_acc= 0.17241 time= 0.00900
Epoch: 0004 train_loss= 2.07963 train_acc= 0.12830 val_loss= 2.06654 val_acc= 0.17241 time= 0.00800
Epoch: 0005 train_loss= 2.09062 train_acc= 0.14717 val_loss= 2.07097 val_acc= 0.24138 time= 0.00700
Epoch: 0006 train_loss= 2.06701 train_acc= 0.12453 val_loss= 2.07537 val_acc= 0.20690 time= 0.00900
Epoch: 0007 train_loss= 2.06178 train_acc= 0.18113 val_loss= 2.07930 val_acc= 0.13793 time= 0.00700
Epoch: 0008 train_loss= 2.05680 train_acc= 0.18868 val_loss= 2.08344 val_acc= 0.13793 time= 0.00800
Epoch: 0009 train_loss= 2.05464 train_acc= 0.14717 val_loss= 2.08694 val_acc= 0.13793 time= 0.00800
Epoch: 0010 train_loss= 2.04655 train_acc= 0.17736 val_loss= 2.08984 val_acc= 0.13793 time= 0.00900
Epoch: 0011 train_loss= 2.04246 train_acc= 0.17736 val_loss= 2.09267 val_acc= 0.13793 time= 0.01100
Epoch: 0012 train_loss= 2.05984 train_acc= 0.17358 val_loss= 2.09487 val_acc= 0.13793 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 2.08941 accuracy= 0.15254 time= 0.00300 
