Epoch: 0001 train_loss= 2.09162 train_acc= 0.12668 val_loss= 2.10856 val_acc= 0.10345 time= 0.75717
Epoch: 0002 train_loss= 2.08585 train_acc= 0.11590 val_loss= 2.10412 val_acc= 0.10345 time= 0.00600
Epoch: 0003 train_loss= 2.07955 train_acc= 0.14286 val_loss= 2.10011 val_acc= 0.03448 time= 0.00600
Epoch: 0004 train_loss= 2.07419 train_acc= 0.11590 val_loss= 2.09652 val_acc= 0.03448 time= 0.00500
Epoch: 0005 train_loss= 2.07160 train_acc= 0.15094 val_loss= 2.09327 val_acc= 0.06897 time= 0.00400
Epoch: 0006 train_loss= 2.06663 train_acc= 0.13747 val_loss= 2.09040 val_acc= 0.06897 time= 0.00500
Epoch: 0007 train_loss= 2.06348 train_acc= 0.17790 val_loss= 2.08789 val_acc= 0.06897 time= 0.00500
Epoch: 0008 train_loss= 2.06052 train_acc= 0.18059 val_loss= 2.08575 val_acc= 0.06897 time= 0.00500
Epoch: 0009 train_loss= 2.05756 train_acc= 0.17251 val_loss= 2.08381 val_acc= 0.06897 time= 0.00500
Epoch: 0010 train_loss= 2.05445 train_acc= 0.18059 val_loss= 2.08207 val_acc= 0.06897 time= 0.00400
Epoch: 0011 train_loss= 2.05557 train_acc= 0.17251 val_loss= 2.08053 val_acc= 0.06897 time= 0.00600
Epoch: 0012 train_loss= 2.05155 train_acc= 0.17251 val_loss= 2.07936 val_acc= 0.06897 time= 0.00500
Epoch: 0013 train_loss= 2.05077 train_acc= 0.17251 val_loss= 2.07845 val_acc= 0.06897 time= 0.00500
Epoch: 0014 train_loss= 2.04927 train_acc= 0.17520 val_loss= 2.07778 val_acc= 0.06897 time= 0.00400
Epoch: 0015 train_loss= 2.04884 train_acc= 0.17520 val_loss= 2.07732 val_acc= 0.06897 time= 0.00600
Epoch: 0016 train_loss= 2.04604 train_acc= 0.17790 val_loss= 2.07679 val_acc= 0.06897 time= 0.00500
Epoch: 0017 train_loss= 2.04398 train_acc= 0.17251 val_loss= 2.07635 val_acc= 0.06897 time= 0.00500
Epoch: 0018 train_loss= 2.04343 train_acc= 0.17790 val_loss= 2.07583 val_acc= 0.06897 time= 0.00600
Epoch: 0019 train_loss= 2.04400 train_acc= 0.17520 val_loss= 2.07512 val_acc= 0.06897 time= 0.00500
Epoch: 0020 train_loss= 2.04571 train_acc= 0.17251 val_loss= 2.07457 val_acc= 0.06897 time= 0.00516
Epoch: 0021 train_loss= 2.04189 train_acc= 0.16981 val_loss= 2.07430 val_acc= 0.06897 time= 0.00600
Epoch: 0022 train_loss= 2.04694 train_acc= 0.17790 val_loss= 2.07437 val_acc= 0.06897 time= 0.00600
Epoch: 0023 train_loss= 2.04290 train_acc= 0.16981 val_loss= 2.07483 val_acc= 0.06897 time= 0.00500
Epoch: 0024 train_loss= 2.04359 train_acc= 0.17251 val_loss= 2.07532 val_acc= 0.06897 time= 0.00500
Epoch: 0025 train_loss= 2.04756 train_acc= 0.16712 val_loss= 2.07609 val_acc= 0.06897 time= 0.00500
Early stopping...
Optimization Finished!
Test set results: cost= 2.08661 accuracy= 0.16949 time= 0.00200 
