Epoch: 0001 train_loss= 1.39966 train_acc= 0.52364 val_loss= 8.62314 val_acc= 0.42623 time= 0.46911
Epoch: 0002 train_loss= 1.02652 train_acc= 0.50364 val_loss= 8.01974 val_acc= 0.42623 time= 0.01400
Epoch: 0003 train_loss= 0.85680 train_acc= 0.47636 val_loss= 7.65375 val_acc= 0.47541 time= 0.01200
Epoch: 0004 train_loss= 0.92649 train_acc= 0.50545 val_loss= 7.40372 val_acc= 0.52459 time= 0.01300
Epoch: 0005 train_loss= 0.84173 train_acc= 0.50545 val_loss= 7.12776 val_acc= 0.52459 time= 0.01500
Epoch: 0006 train_loss= 1.56601 train_acc= 0.49273 val_loss= 6.67167 val_acc= 0.57377 time= 0.01400
Epoch: 0007 train_loss= 0.93275 train_acc= 0.52364 val_loss= 6.21377 val_acc= 0.57377 time= 0.01300
Epoch: 0008 train_loss= 1.05311 train_acc= 0.49455 val_loss= 6.00392 val_acc= 0.55738 time= 0.01400
Epoch: 0009 train_loss= 1.47574 train_acc= 0.48182 val_loss= 5.85224 val_acc= 0.50820 time= 0.01300
Epoch: 0010 train_loss= 1.03857 train_acc= 0.50182 val_loss= 5.57885 val_acc= 0.50820 time= 0.01500
Epoch: 0011 train_loss= 1.12454 train_acc= 0.48182 val_loss= 5.30159 val_acc= 0.50820 time= 0.01500
Epoch: 0012 train_loss= 0.92548 train_acc= 0.50182 val_loss= 5.07296 val_acc= 0.49180 time= 0.01300
Epoch: 0013 train_loss= 0.75894 train_acc= 0.52364 val_loss= 4.82359 val_acc= 0.47541 time= 0.01500
Epoch: 0014 train_loss= 0.93906 train_acc= 0.51455 val_loss= 4.50657 val_acc= 0.47541 time= 0.01400
Epoch: 0015 train_loss= 0.85402 train_acc= 0.47455 val_loss= 4.32359 val_acc= 0.50820 time= 0.01500
Epoch: 0016 train_loss= 0.87023 train_acc= 0.52364 val_loss= 4.28633 val_acc= 0.44262 time= 0.01400
Epoch: 0017 train_loss= 0.77898 train_acc= 0.49818 val_loss= 4.28974 val_acc= 0.40984 time= 0.01500
Epoch: 0018 train_loss= 0.78304 train_acc= 0.53273 val_loss= 4.30818 val_acc= 0.37705 time= 0.01300
Epoch: 0019 train_loss= 0.71773 train_acc= 0.53273 val_loss= 4.33215 val_acc= 0.40984 time= 0.01300
Epoch: 0020 train_loss= 0.80009 train_acc= 0.55273 val_loss= 4.39383 val_acc= 0.39344 time= 0.01300
Epoch: 0021 train_loss= 0.87326 train_acc= 0.52364 val_loss= 4.51558 val_acc= 0.40984 time= 0.01300
Epoch: 0022 train_loss= 0.85549 train_acc= 0.52727 val_loss= 4.51875 val_acc= 0.40984 time= 0.01366
Early stopping...
Optimization Finished!
Test set results: cost= 0.72690 accuracy= 0.48361 time= 0.00635 
