Epoch: 0001 train_loss= 2.08520 train_acc= 0.15723 val_loss= 2.07568 val_acc= 0.17241 time= 0.14063
Epoch: 0002 train_loss= 2.07971 train_acc= 0.15094 val_loss= 2.07003 val_acc= 0.17241 time= 0.00000
Epoch: 0003 train_loss= 2.07612 train_acc= 0.16352 val_loss= 2.06349 val_acc= 0.17241 time= 0.01563
Epoch: 0004 train_loss= 2.07988 train_acc= 0.15723 val_loss= 2.05873 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.06927 train_acc= 0.19497 val_loss= 2.05464 val_acc= 0.17241 time= 0.01563
Epoch: 0006 train_loss= 2.06362 train_acc= 0.15094 val_loss= 2.05047 val_acc= 0.17241 time= 0.00000
Epoch: 0007 train_loss= 2.05906 train_acc= 0.17610 val_loss= 2.04668 val_acc= 0.17241 time= 0.02085
Epoch: 0008 train_loss= 2.05788 train_acc= 0.15094 val_loss= 2.04344 val_acc= 0.10345 time= 0.01050
Epoch: 0009 train_loss= 2.06301 train_acc= 0.15723 val_loss= 2.04095 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.05801 train_acc= 0.18239 val_loss= 2.03863 val_acc= 0.13793 time= 0.01563
Epoch: 0011 train_loss= 2.04857 train_acc= 0.17610 val_loss= 2.03634 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.04744 train_acc= 0.21384 val_loss= 2.03498 val_acc= 0.13793 time= 0.01562
Epoch: 0013 train_loss= 2.04666 train_acc= 0.20755 val_loss= 2.03334 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.04317 train_acc= 0.20126 val_loss= 2.03213 val_acc= 0.17241 time= 0.01563
Epoch: 0015 train_loss= 2.04366 train_acc= 0.21384 val_loss= 2.03075 val_acc= 0.20690 time= 0.01563
Epoch: 0016 train_loss= 2.02848 train_acc= 0.22642 val_loss= 2.02849 val_acc= 0.20690 time= 0.00000
Epoch: 0017 train_loss= 2.04188 train_acc= 0.16981 val_loss= 2.02560 val_acc= 0.20690 time= 0.01563
Epoch: 0018 train_loss= 2.04088 train_acc= 0.18239 val_loss= 2.02338 val_acc= 0.20690 time= 0.00000
Epoch: 0019 train_loss= 2.02612 train_acc= 0.19497 val_loss= 2.02123 val_acc= 0.20690 time= 0.01563
Epoch: 0020 train_loss= 2.04096 train_acc= 0.18868 val_loss= 2.01994 val_acc= 0.20690 time= 0.01563
Epoch: 0021 train_loss= 2.02183 train_acc= 0.21384 val_loss= 2.01951 val_acc= 0.20690 time= 0.00000
Epoch: 0022 train_loss= 2.03862 train_acc= 0.19497 val_loss= 2.01881 val_acc= 0.20690 time= 0.01563
Epoch: 0023 train_loss= 2.02272 train_acc= 0.18868 val_loss= 2.01866 val_acc= 0.20690 time= 0.00000
Epoch: 0024 train_loss= 2.01558 train_acc= 0.20126 val_loss= 2.01855 val_acc= 0.20690 time= 0.01562
Epoch: 0025 train_loss= 2.01524 train_acc= 0.20126 val_loss= 2.01777 val_acc= 0.20690 time= 0.00000
Epoch: 0026 train_loss= 2.02283 train_acc= 0.21384 val_loss= 2.01818 val_acc= 0.20690 time= 0.01563
Epoch: 0027 train_loss= 2.02049 train_acc= 0.23270 val_loss= 2.01948 val_acc= 0.17241 time= 0.01563
Epoch: 0028 train_loss= 2.02835 train_acc= 0.19497 val_loss= 2.02162 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.12800 accuracy= 0.13559 time= 0.00000 
