Epoch: 0001 train_loss= 0.70077 train_acc= 0.51429 val_loss= 0.69756 val_acc= 0.52459 time= 0.46879
Epoch: 0002 train_loss= 0.69756 train_acc= 0.51688 val_loss= 0.69515 val_acc= 0.52459 time= 0.00000
Epoch: 0003 train_loss= 0.69508 train_acc= 0.51688 val_loss= 0.69354 val_acc= 0.52459 time= 0.01562
Epoch: 0004 train_loss= 0.69328 train_acc= 0.51688 val_loss= 0.69257 val_acc= 0.52459 time= 0.01562
Epoch: 0005 train_loss= 0.69230 train_acc= 0.51688 val_loss= 0.69206 val_acc= 0.52459 time= 0.00000
Epoch: 0006 train_loss= 0.69156 train_acc= 0.51688 val_loss= 0.69182 val_acc= 0.52459 time= 0.01563
Epoch: 0007 train_loss= 0.69123 train_acc= 0.51688 val_loss= 0.69176 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69150 train_acc= 0.51688 val_loss= 0.69178 val_acc= 0.52459 time= 0.01562
Epoch: 0009 train_loss= 0.69132 train_acc= 0.51688 val_loss= 0.69180 val_acc= 0.52459 time= 0.00000
Epoch: 0010 train_loss= 0.69090 train_acc= 0.51688 val_loss= 0.69182 val_acc= 0.52459 time= 0.01563
Epoch: 0011 train_loss= 0.69107 train_acc= 0.51688 val_loss= 0.69179 val_acc= 0.52459 time= 0.01563
Epoch: 0012 train_loss= 0.69064 train_acc= 0.51818 val_loss= 0.69171 val_acc= 0.52459 time= 0.01563
Epoch: 0013 train_loss= 0.69085 train_acc= 0.51818 val_loss= 0.69159 val_acc= 0.52459 time= 0.00000
Epoch: 0014 train_loss= 0.69000 train_acc= 0.52208 val_loss= 0.69143 val_acc= 0.54098 time= 0.01563
Epoch: 0015 train_loss= 0.68968 train_acc= 0.51818 val_loss= 0.69126 val_acc= 0.54098 time= 0.01563
Epoch: 0016 train_loss= 0.68984 train_acc= 0.52208 val_loss= 0.69108 val_acc= 0.54098 time= 0.01563
Epoch: 0017 train_loss= 0.68929 train_acc= 0.52338 val_loss= 0.69092 val_acc= 0.54098 time= 0.00000
Epoch: 0018 train_loss= 0.68949 train_acc= 0.52338 val_loss= 0.69078 val_acc= 0.54098 time= 0.01563
Epoch: 0019 train_loss= 0.68909 train_acc= 0.52468 val_loss= 0.69066 val_acc= 0.54098 time= 0.01563
Epoch: 0020 train_loss= 0.68900 train_acc= 0.52338 val_loss= 0.69056 val_acc= 0.54098 time= 0.01562
Epoch: 0021 train_loss= 0.68865 train_acc= 0.52338 val_loss= 0.69048 val_acc= 0.54098 time= 0.00000
Epoch: 0022 train_loss= 0.68817 train_acc= 0.52078 val_loss= 0.69043 val_acc= 0.54098 time= 0.01563
Epoch: 0023 train_loss= 0.68831 train_acc= 0.52468 val_loss= 0.69042 val_acc= 0.54098 time= 0.01563
Epoch: 0024 train_loss= 0.68840 train_acc= 0.52857 val_loss= 0.69046 val_acc= 0.54098 time= 0.01563
Epoch: 0025 train_loss= 0.68810 train_acc= 0.54416 val_loss= 0.69043 val_acc= 0.54098 time= 0.00000
Epoch: 0026 train_loss= 0.68832 train_acc= 0.54026 val_loss= 0.69034 val_acc= 0.54098 time= 0.01563
Epoch: 0027 train_loss= 0.68788 train_acc= 0.52987 val_loss= 0.69031 val_acc= 0.54098 time= 0.01562
Epoch: 0028 train_loss= 0.68744 train_acc= 0.53506 val_loss= 0.69026 val_acc= 0.54098 time= 0.01563
Epoch: 0029 train_loss= 0.68712 train_acc= 0.53247 val_loss= 0.69025 val_acc= 0.54098 time= 0.00000
Epoch: 0030 train_loss= 0.68659 train_acc= 0.55455 val_loss= 0.69011 val_acc= 0.54098 time= 0.01563
Epoch: 0031 train_loss= 0.68697 train_acc= 0.54805 val_loss= 0.68997 val_acc= 0.54098 time= 0.01563
Epoch: 0032 train_loss= 0.68622 train_acc= 0.54675 val_loss= 0.68986 val_acc= 0.54098 time= 0.00000
Epoch: 0033 train_loss= 0.68602 train_acc= 0.54675 val_loss= 0.68977 val_acc= 0.54098 time= 0.01563
Epoch: 0034 train_loss= 0.68546 train_acc= 0.53766 val_loss= 0.68969 val_acc= 0.54098 time= 0.01563
Epoch: 0035 train_loss= 0.68653 train_acc= 0.54675 val_loss= 0.68964 val_acc= 0.55738 time= 0.01563
Epoch: 0036 train_loss= 0.68496 train_acc= 0.56494 val_loss= 0.68953 val_acc= 0.55738 time= 0.00000
Epoch: 0037 train_loss= 0.68589 train_acc= 0.54675 val_loss= 0.68953 val_acc= 0.55738 time= 0.01563
Epoch: 0038 train_loss= 0.68450 train_acc= 0.56364 val_loss= 0.68952 val_acc= 0.55738 time= 0.01563
Epoch: 0039 train_loss= 0.68379 train_acc= 0.57273 val_loss= 0.68937 val_acc= 0.55738 time= 0.00000
Epoch: 0040 train_loss= 0.68489 train_acc= 0.57662 val_loss= 0.68921 val_acc= 0.55738 time= 0.00000
Epoch: 0041 train_loss= 0.68429 train_acc= 0.56364 val_loss= 0.68920 val_acc= 0.55738 time= 0.01563
Epoch: 0042 train_loss= 0.68579 train_acc= 0.53506 val_loss= 0.68908 val_acc= 0.55738 time= 0.01563
Epoch: 0043 train_loss= 0.68601 train_acc= 0.53377 val_loss= 0.68895 val_acc= 0.57377 time= 0.01562
Epoch: 0044 train_loss= 0.68331 train_acc= 0.56104 val_loss= 0.68939 val_acc= 0.63934 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69173 accuracy= 0.59016 time= 0.01563 
