Epoch: 0001 train_loss= 1.39191 train_acc= 0.22801 val_loss= 1.39120 val_acc= 0.19643 time= 0.26607
Epoch: 0002 train_loss= 1.38983 train_acc= 0.23127 val_loss= 1.39046 val_acc= 0.19643 time= 0.01563
Epoch: 0003 train_loss= 1.38994 train_acc= 0.22801 val_loss= 1.38978 val_acc= 0.19643 time= 0.00000
Epoch: 0004 train_loss= 1.38957 train_acc= 0.21173 val_loss= 1.38916 val_acc= 0.19643 time= 0.01563
Epoch: 0005 train_loss= 1.38872 train_acc= 0.23453 val_loss= 1.38857 val_acc= 0.16071 time= 0.00000
Epoch: 0006 train_loss= 1.38828 train_acc= 0.28664 val_loss= 1.38803 val_acc= 0.30357 time= 0.01563
Epoch: 0007 train_loss= 1.38876 train_acc= 0.24430 val_loss= 1.38751 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.38787 train_acc= 0.27687 val_loss= 1.38702 val_acc= 0.30357 time= 0.00000
Epoch: 0009 train_loss= 1.38726 train_acc= 0.26059 val_loss= 1.38656 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.38712 train_acc= 0.27036 val_loss= 1.38612 val_acc= 0.30357 time= 0.00000
Epoch: 0011 train_loss= 1.38636 train_acc= 0.27362 val_loss= 1.38571 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.38691 train_acc= 0.27362 val_loss= 1.38531 val_acc= 0.30357 time= 0.00000
Epoch: 0013 train_loss= 1.38577 train_acc= 0.27362 val_loss= 1.38491 val_acc= 0.30357 time= 0.01563
Epoch: 0014 train_loss= 1.38633 train_acc= 0.27036 val_loss= 1.38453 val_acc= 0.30357 time= 0.00000
Epoch: 0015 train_loss= 1.38600 train_acc= 0.27036 val_loss= 1.38417 val_acc= 0.30357 time= 0.01563
Epoch: 0016 train_loss= 1.38457 train_acc= 0.27362 val_loss= 1.38381 val_acc= 0.30357 time= 0.00000
Epoch: 0017 train_loss= 1.38541 train_acc= 0.27036 val_loss= 1.38346 val_acc= 0.30357 time= 0.01563
Epoch: 0018 train_loss= 1.38461 train_acc= 0.27362 val_loss= 1.38312 val_acc= 0.30357 time= 0.00000
Epoch: 0019 train_loss= 1.38500 train_acc= 0.27036 val_loss= 1.38280 val_acc= 0.30357 time= 0.01563
Epoch: 0020 train_loss= 1.38499 train_acc= 0.27036 val_loss= 1.38249 val_acc= 0.30357 time= 0.00000
Epoch: 0021 train_loss= 1.38388 train_acc= 0.27362 val_loss= 1.38220 val_acc= 0.30357 time= 0.01563
Epoch: 0022 train_loss= 1.38427 train_acc= 0.27036 val_loss= 1.38192 val_acc= 0.30357 time= 0.01563
Epoch: 0023 train_loss= 1.38504 train_acc= 0.27036 val_loss= 1.38168 val_acc= 0.30357 time= 0.00000
Epoch: 0024 train_loss= 1.38385 train_acc= 0.27036 val_loss= 1.38144 val_acc= 0.30357 time= 0.01563
Epoch: 0025 train_loss= 1.38391 train_acc= 0.27036 val_loss= 1.38122 val_acc= 0.30357 time= 0.00000
Epoch: 0026 train_loss= 1.38294 train_acc= 0.27036 val_loss= 1.38105 val_acc= 0.30357 time= 0.01563
Epoch: 0027 train_loss= 1.38394 train_acc= 0.27036 val_loss= 1.38090 val_acc= 0.30357 time= 0.00000
Epoch: 0028 train_loss= 1.38383 train_acc= 0.27036 val_loss= 1.38079 val_acc= 0.30357 time= 0.01563
Epoch: 0029 train_loss= 1.38274 train_acc= 0.27036 val_loss= 1.38071 val_acc= 0.30357 time= 0.00000
Epoch: 0030 train_loss= 1.38337 train_acc= 0.27036 val_loss= 1.38066 val_acc= 0.30357 time= 0.01563
Epoch: 0031 train_loss= 1.38385 train_acc= 0.27036 val_loss= 1.38064 val_acc= 0.30357 time= 0.00000
Epoch: 0032 train_loss= 1.38296 train_acc= 0.27036 val_loss= 1.38061 val_acc= 0.30357 time= 0.01563
Epoch: 0033 train_loss= 1.38216 train_acc= 0.27036 val_loss= 1.38058 val_acc= 0.30357 time= 0.00000
Epoch: 0034 train_loss= 1.38459 train_acc= 0.27036 val_loss= 1.38058 val_acc= 0.30357 time= 0.01563
Epoch: 0035 train_loss= 1.38322 train_acc= 0.27036 val_loss= 1.38058 val_acc= 0.30357 time= 0.00000
Epoch: 0036 train_loss= 1.38377 train_acc= 0.27036 val_loss= 1.38060 val_acc= 0.30357 time= 0.01563
Epoch: 0037 train_loss= 1.38208 train_acc= 0.27036 val_loss= 1.38063 val_acc= 0.30357 time= 0.00000
Epoch: 0038 train_loss= 1.38302 train_acc= 0.27036 val_loss= 1.38067 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37408 accuracy= 0.29204 time= 0.00000 
