Epoch: 0001 train_loss= 2.16174 train_acc= 0.11590 val_loss= 2.03173 val_acc= 0.20690 time= 0.73317
Epoch: 0002 train_loss= 2.11589 train_acc= 0.11860 val_loss= 2.03150 val_acc= 0.20690 time= 0.00900
Epoch: 0003 train_loss= 2.10833 train_acc= 0.11860 val_loss= 2.03495 val_acc= 0.10345 time= 0.00800
Epoch: 0004 train_loss= 2.10854 train_acc= 0.14286 val_loss= 2.03861 val_acc= 0.06897 time= 0.00800
Epoch: 0005 train_loss= 2.08575 train_acc= 0.13208 val_loss= 2.04366 val_acc= 0.17241 time= 0.00900
Epoch: 0006 train_loss= 2.06902 train_acc= 0.18329 val_loss= 2.04884 val_acc= 0.17241 time= 0.00800
Epoch: 0007 train_loss= 2.06813 train_acc= 0.16712 val_loss= 2.05392 val_acc= 0.17241 time= 0.00700
Epoch: 0008 train_loss= 2.07803 train_acc= 0.16981 val_loss= 2.05886 val_acc= 0.17241 time= 0.00800
Epoch: 0009 train_loss= 2.07230 train_acc= 0.17520 val_loss= 2.06385 val_acc= 0.17241 time= 0.00800
Epoch: 0010 train_loss= 2.06098 train_acc= 0.16981 val_loss= 2.06893 val_acc= 0.13793 time= 0.00800
Epoch: 0011 train_loss= 2.05160 train_acc= 0.18059 val_loss= 2.07393 val_acc= 0.13793 time= 0.00800
Epoch: 0012 train_loss= 2.06524 train_acc= 0.16981 val_loss= 2.07758 val_acc= 0.13793 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 2.04813 accuracy= 0.20339 time= 0.00300 
