Epoch: 0001 train_loss= 2.08091 train_acc= 0.18239 val_loss= 2.09505 val_acc= 0.13793 time= 0.09376
Epoch: 0002 train_loss= 2.07870 train_acc= 0.18239 val_loss= 2.09860 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.07599 train_acc= 0.18239 val_loss= 2.10333 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.07309 train_acc= 0.18239 val_loss= 2.10890 val_acc= 0.13793 time= 0.01563
Epoch: 0005 train_loss= 2.07018 train_acc= 0.18239 val_loss= 2.11530 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.06925 train_acc= 0.18239 val_loss= 2.12217 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.06566 train_acc= 0.18239 val_loss= 2.12968 val_acc= 0.13793 time= 0.01562
Epoch: 0008 train_loss= 2.06409 train_acc= 0.18239 val_loss= 2.13730 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.06242 train_acc= 0.18239 val_loss= 2.14486 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06189 train_acc= 0.18239 val_loss= 2.15155 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.06017 train_acc= 0.18239 val_loss= 2.15773 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.05945 train_acc= 0.18239 val_loss= 2.16341 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07952 accuracy= 0.16949 time= 0.00000 
