Epoch: 0001 train_loss= 2.21938 train_acc= 0.07817 val_loss= 2.07624 val_acc= 0.13793 time= 0.98339
Epoch: 0002 train_loss= 2.10777 train_acc= 0.09704 val_loss= 2.06607 val_acc= 0.17241 time= 0.01563
Epoch: 0003 train_loss= 2.09043 train_acc= 0.11590 val_loss= 2.05729 val_acc= 0.17241 time= 0.01563
Epoch: 0004 train_loss= 2.09733 train_acc= 0.16981 val_loss= 2.06245 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.07930 train_acc= 0.16173 val_loss= 2.06999 val_acc= 0.17241 time= 0.01563
Epoch: 0006 train_loss= 2.06639 train_acc= 0.16442 val_loss= 2.07417 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.05808 train_acc= 0.17520 val_loss= 2.08029 val_acc= 0.10345 time= 0.03011
Epoch: 0008 train_loss= 2.05915 train_acc= 0.17520 val_loss= 2.08707 val_acc= 0.10345 time= 0.02053
Epoch: 0009 train_loss= 2.05716 train_acc= 0.18598 val_loss= 2.09440 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.05513 train_acc= 0.18059 val_loss= 2.09535 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.05606 train_acc= 0.16173 val_loss= 2.09315 val_acc= 0.06897 time= 0.01563
Epoch: 0012 train_loss= 2.06005 train_acc= 0.20755 val_loss= 2.08758 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07954 accuracy= 0.15254 time= 0.01563 
