Epoch: 0001 train_loss= 0.69868 train_acc= 0.52182 val_loss= 0.69885 val_acc= 0.47541 time= 0.29669
Epoch: 0002 train_loss= 0.69773 train_acc= 0.53818 val_loss= 0.69874 val_acc= 0.47541 time= 0.01563
Epoch: 0003 train_loss= 0.69672 train_acc= 0.53818 val_loss= 0.69874 val_acc= 0.47541 time= 0.01562
Epoch: 0004 train_loss= 0.69604 train_acc= 0.53455 val_loss= 0.69877 val_acc= 0.47541 time= 0.01563
Epoch: 0005 train_loss= 0.69504 train_acc= 0.53455 val_loss= 0.69889 val_acc= 0.47541 time= 0.00000
Epoch: 0006 train_loss= 0.69551 train_acc= 0.53273 val_loss= 0.69903 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69477 train_acc= 0.53636 val_loss= 0.69913 val_acc= 0.47541 time= 0.01563
Epoch: 0008 train_loss= 0.69493 train_acc= 0.53455 val_loss= 0.69925 val_acc= 0.47541 time= 0.01563
Epoch: 0009 train_loss= 0.69417 train_acc= 0.53818 val_loss= 0.69930 val_acc= 0.47541 time= 0.00000
Epoch: 0010 train_loss= 0.69430 train_acc= 0.52909 val_loss= 0.69922 val_acc= 0.47541 time= 0.01563
Epoch: 0011 train_loss= 0.69358 train_acc= 0.53455 val_loss= 0.69913 val_acc= 0.47541 time= 0.01563
Epoch: 0012 train_loss= 0.69298 train_acc= 0.53636 val_loss= 0.69889 val_acc= 0.47541 time= 0.01562
Epoch: 0013 train_loss= 0.69338 train_acc= 0.53091 val_loss= 0.69855 val_acc= 0.47541 time= 0.00000
Epoch: 0014 train_loss= 0.69250 train_acc= 0.53273 val_loss= 0.69820 val_acc= 0.47541 time= 0.01563
Epoch: 0015 train_loss= 0.69290 train_acc= 0.53455 val_loss= 0.69791 val_acc= 0.47541 time= 0.01563
Epoch: 0016 train_loss= 0.69249 train_acc= 0.53636 val_loss= 0.69749 val_acc= 0.47541 time= 0.00000
Epoch: 0017 train_loss= 0.69235 train_acc= 0.53455 val_loss= 0.69718 val_acc= 0.47541 time= 0.01563
Epoch: 0018 train_loss= 0.69266 train_acc= 0.53636 val_loss= 0.69697 val_acc= 0.47541 time= 0.01562
Epoch: 0019 train_loss= 0.69199 train_acc= 0.53818 val_loss= 0.69690 val_acc= 0.47541 time= 0.01563
Epoch: 0020 train_loss= 0.69158 train_acc= 0.53455 val_loss= 0.69692 val_acc= 0.47541 time= 0.00000
Epoch: 0021 train_loss= 0.69110 train_acc= 0.53455 val_loss= 0.69690 val_acc= 0.47541 time= 0.01563
Epoch: 0022 train_loss= 0.69098 train_acc= 0.53455 val_loss= 0.69691 val_acc= 0.47541 time= 0.01563
Epoch: 0023 train_loss= 0.69204 train_acc= 0.53273 val_loss= 0.69683 val_acc= 0.47541 time= 0.01563
Epoch: 0024 train_loss= 0.69261 train_acc= 0.53455 val_loss= 0.69668 val_acc= 0.47541 time= 0.00000
Epoch: 0025 train_loss= 0.69166 train_acc= 0.53455 val_loss= 0.69652 val_acc= 0.47541 time= 0.01563
Epoch: 0026 train_loss= 0.69085 train_acc= 0.53455 val_loss= 0.69643 val_acc= 0.47541 time= 0.01563
Epoch: 0027 train_loss= 0.69039 train_acc= 0.53455 val_loss= 0.69648 val_acc= 0.47541 time= 0.00000
Epoch: 0028 train_loss= 0.69085 train_acc= 0.53455 val_loss= 0.69656 val_acc= 0.47541 time= 0.01563
Epoch: 0029 train_loss= 0.69155 train_acc= 0.53455 val_loss= 0.69659 val_acc= 0.47541 time= 0.01563
Epoch: 0030 train_loss= 0.69157 train_acc= 0.53455 val_loss= 0.69660 val_acc= 0.47541 time= 0.00000
Epoch: 0031 train_loss= 0.69151 train_acc= 0.53455 val_loss= 0.69655 val_acc= 0.47541 time= 0.01563
Epoch: 0032 train_loss= 0.69077 train_acc= 0.53636 val_loss= 0.69652 val_acc= 0.47541 time= 0.01563
Epoch: 0033 train_loss= 0.69060 train_acc= 0.53455 val_loss= 0.69647 val_acc= 0.47541 time= 0.01563
Epoch: 0034 train_loss= 0.69158 train_acc= 0.53455 val_loss= 0.69641 val_acc= 0.47541 time= 0.00000
Epoch: 0035 train_loss= 0.69090 train_acc= 0.53455 val_loss= 0.69630 val_acc= 0.47541 time= 0.01563
Epoch: 0036 train_loss= 0.69126 train_acc= 0.53455 val_loss= 0.69618 val_acc= 0.47541 time= 0.01563
Epoch: 0037 train_loss= 0.69106 train_acc= 0.53455 val_loss= 0.69610 val_acc= 0.47541 time= 0.00000
Epoch: 0038 train_loss= 0.69142 train_acc= 0.53455 val_loss= 0.69598 val_acc= 0.47541 time= 0.01563
Epoch: 0039 train_loss= 0.69054 train_acc= 0.53455 val_loss= 0.69586 val_acc= 0.47541 time= 0.01563
Epoch: 0040 train_loss= 0.69060 train_acc= 0.53455 val_loss= 0.69581 val_acc= 0.47541 time= 0.00000
Epoch: 0041 train_loss= 0.69167 train_acc= 0.53455 val_loss= 0.69575 val_acc= 0.47541 time= 0.01562
Epoch: 0042 train_loss= 0.69102 train_acc= 0.53455 val_loss= 0.69570 val_acc= 0.47541 time= 0.01563
Epoch: 0043 train_loss= 0.69052 train_acc= 0.53455 val_loss= 0.69578 val_acc= 0.47541 time= 0.01563
Epoch: 0044 train_loss= 0.69097 train_acc= 0.53636 val_loss= 0.69589 val_acc= 0.47541 time= 0.00000
Epoch: 0045 train_loss= 0.69051 train_acc= 0.53455 val_loss= 0.69606 val_acc= 0.47541 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.70431 accuracy= 0.44262 time= 0.00000 
