Epoch: 0001 train_loss= 2.09406 train_acc= 0.12830 val_loss= 2.05829 val_acc= 0.41379 time= 0.50144
Epoch: 0002 train_loss= 2.08928 train_acc= 0.13585 val_loss= 2.05804 val_acc= 0.41379 time= 0.00600
Epoch: 0003 train_loss= 2.08571 train_acc= 0.13585 val_loss= 2.05790 val_acc= 0.41379 time= 0.00500
Epoch: 0004 train_loss= 2.08172 train_acc= 0.12830 val_loss= 2.05778 val_acc= 0.34483 time= 0.00500
Epoch: 0005 train_loss= 2.07724 train_acc= 0.13585 val_loss= 2.05777 val_acc= 0.06897 time= 0.00400
Epoch: 0006 train_loss= 2.07380 train_acc= 0.10189 val_loss= 2.05766 val_acc= 0.10345 time= 0.00600
Epoch: 0007 train_loss= 2.07047 train_acc= 0.15472 val_loss= 2.05752 val_acc= 0.10345 time= 0.00500
Epoch: 0008 train_loss= 2.06648 train_acc= 0.17736 val_loss= 2.05732 val_acc= 0.10345 time= 0.00600
Epoch: 0009 train_loss= 2.06388 train_acc= 0.17358 val_loss= 2.05728 val_acc= 0.10345 time= 0.00500
Epoch: 0010 train_loss= 2.05882 train_acc= 0.17358 val_loss= 2.05694 val_acc= 0.10345 time= 0.00500
Epoch: 0011 train_loss= 2.05563 train_acc= 0.17358 val_loss= 2.05645 val_acc= 0.10345 time= 0.00500
Epoch: 0012 train_loss= 2.05265 train_acc= 0.16981 val_loss= 2.05564 val_acc= 0.10345 time= 0.00600
Epoch: 0013 train_loss= 2.04903 train_acc= 0.17358 val_loss= 2.05463 val_acc= 0.10345 time= 0.00500
Epoch: 0014 train_loss= 2.04333 train_acc= 0.17358 val_loss= 2.05333 val_acc= 0.10345 time= 0.00600
Epoch: 0015 train_loss= 2.04413 train_acc= 0.17358 val_loss= 2.05160 val_acc= 0.10345 time= 0.00500
Epoch: 0016 train_loss= 2.04134 train_acc= 0.17358 val_loss= 2.04951 val_acc= 0.10345 time= 0.00500
Epoch: 0017 train_loss= 2.03528 train_acc= 0.17358 val_loss= 2.04742 val_acc= 0.10345 time= 0.00600
Epoch: 0018 train_loss= 2.03841 train_acc= 0.17358 val_loss= 2.04531 val_acc= 0.10345 time= 0.00500
Epoch: 0019 train_loss= 2.03419 train_acc= 0.17358 val_loss= 2.04298 val_acc= 0.10345 time= 0.00500
Epoch: 0020 train_loss= 2.03045 train_acc= 0.17358 val_loss= 2.04042 val_acc= 0.10345 time= 0.00400
Epoch: 0021 train_loss= 2.03305 train_acc= 0.17358 val_loss= 2.03767 val_acc= 0.10345 time= 0.00600
Epoch: 0022 train_loss= 2.03342 train_acc= 0.17358 val_loss= 2.03558 val_acc= 0.10345 time= 0.00500
Epoch: 0023 train_loss= 2.02999 train_acc= 0.17358 val_loss= 2.03356 val_acc= 0.10345 time= 0.00500
Epoch: 0024 train_loss= 2.03077 train_acc= 0.17358 val_loss= 2.03198 val_acc= 0.10345 time= 0.00500
Epoch: 0025 train_loss= 2.03397 train_acc= 0.17358 val_loss= 2.03063 val_acc= 0.10345 time= 0.00500
Epoch: 0026 train_loss= 2.03101 train_acc= 0.17358 val_loss= 2.02934 val_acc= 0.10345 time= 0.00500
Epoch: 0027 train_loss= 2.03374 train_acc= 0.17358 val_loss= 2.02812 val_acc= 0.10345 time= 0.00600
Epoch: 0028 train_loss= 2.02688 train_acc= 0.17358 val_loss= 2.02726 val_acc= 0.10345 time= 0.00400
Epoch: 0029 train_loss= 2.02886 train_acc= 0.17358 val_loss= 2.02706 val_acc= 0.10345 time= 0.00500
Epoch: 0030 train_loss= 2.03101 train_acc= 0.17358 val_loss= 2.02741 val_acc= 0.10345 time= 0.00600
Epoch: 0031 train_loss= 2.02674 train_acc= 0.16981 val_loss= 2.02759 val_acc= 0.10345 time= 0.00500
Epoch: 0032 train_loss= 2.02921 train_acc= 0.17736 val_loss= 2.02817 val_acc= 0.10345 time= 0.00400
Epoch: 0033 train_loss= 2.02870 train_acc= 0.19245 val_loss= 2.02919 val_acc= 0.13793 time= 0.00600
Early stopping...
Optimization Finished!
Test set results: cost= 2.09231 accuracy= 0.11864 time= 0.00200 
