Epoch: 0001 train_loss= 0.92362 train_acc= 0.50519 val_loss= 0.79936 val_acc= 0.59016 time= 0.70516
Epoch: 0002 train_loss= 1.26173 train_acc= 0.45844 val_loss= 0.70356 val_acc= 0.63934 time= 0.01510
Epoch: 0003 train_loss= 1.23122 train_acc= 0.48831 val_loss= 0.70786 val_acc= 0.52459 time= 0.01425
Epoch: 0004 train_loss= 0.78928 train_acc= 0.49221 val_loss= 0.80337 val_acc= 0.49180 time= 0.01306
Epoch: 0005 train_loss= 0.93599 train_acc= 0.47403 val_loss= 0.85459 val_acc= 0.49180 time= 0.01314
Epoch: 0006 train_loss= 0.83779 train_acc= 0.53636 val_loss= 0.88467 val_acc= 0.49180 time= 0.01409
Epoch: 0007 train_loss= 0.96861 train_acc= 0.50130 val_loss= 0.86816 val_acc= 0.49180 time= 0.01021
Epoch: 0008 train_loss= 0.83265 train_acc= 0.50909 val_loss= 0.82906 val_acc= 0.49180 time= 0.01466
Epoch: 0009 train_loss= 1.00199 train_acc= 0.48182 val_loss= 0.80381 val_acc= 0.49180 time= 0.01400
Epoch: 0010 train_loss= 0.79979 train_acc= 0.49870 val_loss= 0.78639 val_acc= 0.49180 time= 0.01296
Epoch: 0011 train_loss= 0.84341 train_acc= 0.49740 val_loss= 0.76122 val_acc= 0.47541 time= 0.00689
Epoch: 0012 train_loss= 0.82124 train_acc= 0.53247 val_loss= 0.73348 val_acc= 0.47541 time= 0.01562
Epoch: 0013 train_loss= 0.78110 train_acc= 0.49610 val_loss= 0.71351 val_acc= 0.57377 time= 0.01872
Epoch: 0014 train_loss= 0.88741 train_acc= 0.51948 val_loss= 0.70334 val_acc= 0.54098 time= 0.01516
Epoch: 0015 train_loss= 0.95833 train_acc= 0.48312 val_loss= 0.69840 val_acc= 0.49180 time= 0.01500
Epoch: 0016 train_loss= 0.74419 train_acc= 0.52468 val_loss= 0.69567 val_acc= 0.54098 time= 0.01535
Epoch: 0017 train_loss= 0.83863 train_acc= 0.54416 val_loss= 0.69212 val_acc= 0.55738 time= 0.01400
Epoch: 0018 train_loss= 0.83505 train_acc= 0.53766 val_loss= 0.69126 val_acc= 0.52459 time= 0.01400
Epoch: 0019 train_loss= 0.76389 train_acc= 0.48442 val_loss= 0.69413 val_acc= 0.50820 time= 0.01600
Epoch: 0020 train_loss= 0.83631 train_acc= 0.50390 val_loss= 0.69753 val_acc= 0.52459 time= 0.01700
Epoch: 0021 train_loss= 0.73322 train_acc= 0.49870 val_loss= 0.70139 val_acc= 0.52459 time= 0.01428
Epoch: 0022 train_loss= 0.74395 train_acc= 0.49091 val_loss= 0.70455 val_acc= 0.50820 time= 0.01600
Early stopping...
Optimization Finished!
Test set results: cost= 0.78991 accuracy= 0.52459 time= 0.00769 
