Epoch: 0001 train_loss= 2.09471 train_acc= 0.10063 val_loss= 2.07760 val_acc= 0.13793 time= 0.18751
Epoch: 0002 train_loss= 2.08759 train_acc= 0.14465 val_loss= 2.07597 val_acc= 0.17241 time= 0.01562
Epoch: 0003 train_loss= 2.08363 train_acc= 0.20126 val_loss= 2.07455 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08087 train_acc= 0.16981 val_loss= 2.07337 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.07563 train_acc= 0.16981 val_loss= 2.07241 val_acc= 0.17241 time= 0.01562
Epoch: 0006 train_loss= 2.07357 train_acc= 0.16981 val_loss= 2.07149 val_acc= 0.17241 time= 0.00000
Epoch: 0007 train_loss= 2.07144 train_acc= 0.16981 val_loss= 2.07077 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.06666 train_acc= 0.16981 val_loss= 2.07023 val_acc= 0.17241 time= 0.01563
Epoch: 0009 train_loss= 2.06617 train_acc= 0.16981 val_loss= 2.06991 val_acc= 0.17241 time= 0.00000
Epoch: 0010 train_loss= 2.06488 train_acc= 0.16981 val_loss= 2.06971 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.06063 train_acc= 0.16981 val_loss= 2.06966 val_acc= 0.17241 time= 0.01563
Epoch: 0012 train_loss= 2.05939 train_acc= 0.16981 val_loss= 2.06974 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.05741 train_acc= 0.16981 val_loss= 2.07010 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.05318 train_acc= 0.16981 val_loss= 2.07069 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.05255 train_acc= 0.16981 val_loss= 2.07153 val_acc= 0.17241 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.05176 accuracy= 0.18644 time= 0.00000 
