Epoch: 0001 train_loss= 1.38778 train_acc= 0.28990 val_loss= 1.38683 val_acc= 0.28333 time= 0.26564
Epoch: 0002 train_loss= 1.38835 train_acc= 0.30619 val_loss= 1.38663 val_acc= 0.28333 time= 0.00000
Epoch: 0003 train_loss= 1.38683 train_acc= 0.31922 val_loss= 1.38643 val_acc= 0.28333 time= 0.01563
Epoch: 0004 train_loss= 1.38650 train_acc= 0.30619 val_loss= 1.38623 val_acc= 0.28333 time= 0.00000
Epoch: 0005 train_loss= 1.38718 train_acc= 0.30619 val_loss= 1.38603 val_acc= 0.28333 time= 0.01563
Epoch: 0006 train_loss= 1.38681 train_acc= 0.30945 val_loss= 1.38584 val_acc= 0.28333 time= 0.00000
Epoch: 0007 train_loss= 1.38653 train_acc= 0.30945 val_loss= 1.38565 val_acc= 0.28333 time= 0.01563
Epoch: 0008 train_loss= 1.38568 train_acc= 0.30945 val_loss= 1.38544 val_acc= 0.28333 time= 0.00000
Epoch: 0009 train_loss= 1.38577 train_acc= 0.30619 val_loss= 1.38523 val_acc= 0.28333 time= 0.01563
Epoch: 0010 train_loss= 1.38408 train_acc= 0.30945 val_loss= 1.38498 val_acc= 0.28333 time= 0.00000
Epoch: 0011 train_loss= 1.38447 train_acc= 0.30619 val_loss= 1.38471 val_acc= 0.28333 time= 0.01563
Epoch: 0012 train_loss= 1.38417 train_acc= 0.30619 val_loss= 1.38443 val_acc= 0.28333 time= 0.00000
Epoch: 0013 train_loss= 1.38395 train_acc= 0.30619 val_loss= 1.38413 val_acc= 0.28333 time= 0.01563
Epoch: 0014 train_loss= 1.38323 train_acc= 0.30619 val_loss= 1.38382 val_acc= 0.28333 time= 0.00000
Epoch: 0015 train_loss= 1.38203 train_acc= 0.30619 val_loss= 1.38350 val_acc= 0.28333 time= 0.01563
Epoch: 0016 train_loss= 1.38204 train_acc= 0.30619 val_loss= 1.38317 val_acc= 0.28333 time= 0.00000
Epoch: 0017 train_loss= 1.38191 train_acc= 0.30619 val_loss= 1.38282 val_acc= 0.28333 time= 0.01563
Epoch: 0018 train_loss= 1.38249 train_acc= 0.30619 val_loss= 1.38249 val_acc= 0.28333 time= 0.00000
Epoch: 0019 train_loss= 1.37942 train_acc= 0.30945 val_loss= 1.38216 val_acc= 0.28333 time= 0.01563
Epoch: 0020 train_loss= 1.37948 train_acc= 0.30619 val_loss= 1.38183 val_acc= 0.28333 time= 0.00000
Epoch: 0021 train_loss= 1.37874 train_acc= 0.30945 val_loss= 1.38150 val_acc= 0.28333 time= 0.00000
Epoch: 0022 train_loss= 1.37789 train_acc= 0.30945 val_loss= 1.38119 val_acc= 0.28333 time= 0.01563
Epoch: 0023 train_loss= 1.37624 train_acc= 0.30619 val_loss= 1.38090 val_acc= 0.28333 time= 0.00000
Epoch: 0024 train_loss= 1.37694 train_acc= 0.30619 val_loss= 1.38064 val_acc= 0.28333 time= 0.01563
Epoch: 0025 train_loss= 1.37862 train_acc= 0.30619 val_loss= 1.38043 val_acc= 0.28333 time= 0.00000
Epoch: 0026 train_loss= 1.37403 train_acc= 0.30945 val_loss= 1.38024 val_acc= 0.28333 time= 0.00000
Epoch: 0027 train_loss= 1.37454 train_acc= 0.30619 val_loss= 1.38009 val_acc= 0.28333 time= 0.01563
Epoch: 0028 train_loss= 1.37287 train_acc= 0.30619 val_loss= 1.38001 val_acc= 0.28333 time= 0.00000
Epoch: 0029 train_loss= 1.37449 train_acc= 0.30619 val_loss= 1.37999 val_acc= 0.28333 time= 0.01563
Epoch: 0030 train_loss= 1.37169 train_acc= 0.30945 val_loss= 1.38000 val_acc= 0.28333 time= 0.00000
Epoch: 0031 train_loss= 1.37203 train_acc= 0.30945 val_loss= 1.38009 val_acc= 0.28333 time= 0.01563
Epoch: 0032 train_loss= 1.36891 train_acc= 0.30619 val_loss= 1.38027 val_acc= 0.28333 time= 0.00000
Epoch: 0033 train_loss= 1.37176 train_acc= 0.30619 val_loss= 1.38057 val_acc= 0.28333 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38490 accuracy= 0.31667 time= 0.00000 
