Epoch: 0001 train_loss= 1.90801 train_acc= 0.25781 val_loss= 1.44095 val_acc= 0.42857 time= 0.62511
Epoch: 0002 train_loss= 1.57405 train_acc= 0.25781 val_loss= 1.53799 val_acc= 0.30357 time= 0.03125
Epoch: 0003 train_loss= 1.61436 train_acc= 0.25000 val_loss= 1.52093 val_acc= 0.28571 time= 0.03125
Epoch: 0004 train_loss= 1.73352 train_acc= 0.24609 val_loss= 1.76241 val_acc= 0.30357 time= 0.01563
Epoch: 0005 train_loss= 1.39736 train_acc= 0.26367 val_loss= 1.76797 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.56411 train_acc= 0.22070 val_loss= 1.71368 val_acc= 0.32143 time= 0.03125
Epoch: 0007 train_loss= 1.40450 train_acc= 0.27539 val_loss= 1.64507 val_acc= 0.35714 time= 0.01562
Epoch: 0008 train_loss= 1.69517 train_acc= 0.26758 val_loss= 1.55920 val_acc= 0.39286 time= 0.03125
Epoch: 0009 train_loss= 1.48506 train_acc= 0.29102 val_loss= 1.47484 val_acc= 0.41071 time= 0.01563
Epoch: 0010 train_loss= 1.39357 train_acc= 0.28906 val_loss= 1.39328 val_acc= 0.41071 time= 0.03126
Epoch: 0011 train_loss= 1.39164 train_acc= 0.30859 val_loss= 1.36940 val_acc= 0.41071 time= 0.01562
Epoch: 0012 train_loss= 1.37973 train_acc= 0.31445 val_loss= 1.35309 val_acc= 0.37500 time= 0.01563
Epoch: 0013 train_loss= 1.39712 train_acc= 0.26953 val_loss= 1.35265 val_acc= 0.37500 time= 0.03125
Epoch: 0014 train_loss= 1.46707 train_acc= 0.32812 val_loss= 1.35295 val_acc= 0.37500 time= 0.01563
Epoch: 0015 train_loss= 1.37665 train_acc= 0.30859 val_loss= 1.35330 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.39439 train_acc= 0.26172 val_loss= 1.35397 val_acc= 0.35714 time= 0.03125
Epoch: 0017 train_loss= 1.37973 train_acc= 0.32422 val_loss= 1.35461 val_acc= 0.35714 time= 0.01563
Epoch: 0018 train_loss= 1.38408 train_acc= 0.28320 val_loss= 1.35527 val_acc= 0.35714 time= 0.03125
Epoch: 0019 train_loss= 1.38515 train_acc= 0.27930 val_loss= 1.35543 val_acc= 0.35714 time= 0.01563
Epoch: 0020 train_loss= 1.37256 train_acc= 0.28906 val_loss= 1.35562 val_acc= 0.35714 time= 0.01562
Epoch: 0021 train_loss= 1.67557 train_acc= 0.29688 val_loss= 1.35536 val_acc= 0.35714 time= 0.03125
Epoch: 0022 train_loss= 1.39344 train_acc= 0.30469 val_loss= 1.35496 val_acc= 0.35714 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38212 accuracy= 0.30088 time= 0.01562 
