Epoch: 0001 train_loss= 2.08697 train_acc= 0.15723 val_loss= 2.08436 val_acc= 0.20690 time= 0.12501
Epoch: 0002 train_loss= 2.08525 train_acc= 0.15723 val_loss= 2.08309 val_acc= 0.20690 time= 0.00000
Epoch: 0003 train_loss= 2.08280 train_acc= 0.12579 val_loss= 2.08214 val_acc= 0.20690 time= 0.01563
Epoch: 0004 train_loss= 2.08088 train_acc= 0.13208 val_loss= 2.08109 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.07936 train_acc= 0.15094 val_loss= 2.07989 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.07787 train_acc= 0.13836 val_loss= 2.07849 val_acc= 0.20690 time= 0.00000
Epoch: 0007 train_loss= 2.07626 train_acc= 0.11950 val_loss= 2.07698 val_acc= 0.17241 time= 0.01563
Epoch: 0008 train_loss= 2.07336 train_acc= 0.14465 val_loss= 2.07533 val_acc= 0.17241 time= 0.01929
Epoch: 0009 train_loss= 2.07152 train_acc= 0.15094 val_loss= 2.07359 val_acc= 0.17241 time= 0.00202
Epoch: 0010 train_loss= 2.06850 train_acc= 0.16981 val_loss= 2.07177 val_acc= 0.17241 time= 0.01000
Epoch: 0011 train_loss= 2.06849 train_acc= 0.15094 val_loss= 2.06999 val_acc= 0.17241 time= 0.00000
Epoch: 0012 train_loss= 2.06609 train_acc= 0.18239 val_loss= 2.06828 val_acc= 0.17241 time= 0.01563
Epoch: 0013 train_loss= 2.06469 train_acc= 0.16352 val_loss= 2.06677 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.06419 train_acc= 0.14465 val_loss= 2.06547 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.06161 train_acc= 0.15723 val_loss= 2.06449 val_acc= 0.17241 time= 0.01563
Epoch: 0016 train_loss= 2.05811 train_acc= 0.16352 val_loss= 2.06369 val_acc= 0.17241 time= 0.00000
Epoch: 0017 train_loss= 2.05906 train_acc= 0.15094 val_loss= 2.06322 val_acc= 0.17241 time= 0.01563
Epoch: 0018 train_loss= 2.05704 train_acc= 0.15094 val_loss= 2.06312 val_acc= 0.17241 time= 0.00000
Epoch: 0019 train_loss= 2.05592 train_acc= 0.15723 val_loss= 2.06328 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 2.05554 train_acc= 0.15094 val_loss= 2.06381 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.05494 train_acc= 0.14465 val_loss= 2.06460 val_acc= 0.17241 time= 0.01563
Epoch: 0022 train_loss= 2.05518 train_acc= 0.16352 val_loss= 2.06569 val_acc= 0.17241 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07939 accuracy= 0.16949 time= 0.01563 
