Epoch: 0001 train_loss= 0.69924 train_acc= 0.51515 val_loss= 0.69783 val_acc= 0.55738 time= 0.12529
Epoch: 0002 train_loss= 0.69829 train_acc= 0.52727 val_loss= 0.69646 val_acc= 0.55738 time= 0.00000
Epoch: 0003 train_loss= 0.69812 train_acc= 0.52424 val_loss= 0.69533 val_acc= 0.55738 time= 0.01533
Epoch: 0004 train_loss= 0.69717 train_acc= 0.52424 val_loss= 0.69433 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69732 train_acc= 0.52121 val_loss= 0.69347 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.69669 train_acc= 0.53333 val_loss= 0.69272 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69643 train_acc= 0.52727 val_loss= 0.69206 val_acc= 0.55738 time= 0.02123
Epoch: 0008 train_loss= 0.69600 train_acc= 0.53030 val_loss= 0.69151 val_acc= 0.55738 time= 0.01724
Epoch: 0009 train_loss= 0.69539 train_acc= 0.53030 val_loss= 0.69103 val_acc= 0.55738 time= 0.01295
Epoch: 0010 train_loss= 0.69478 train_acc= 0.53333 val_loss= 0.69060 val_acc= 0.55738 time= 0.00250
Epoch: 0011 train_loss= 0.69492 train_acc= 0.53030 val_loss= 0.69024 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 0.69465 train_acc= 0.52727 val_loss= 0.68996 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.69449 train_acc= 0.52727 val_loss= 0.68977 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.69447 train_acc= 0.53030 val_loss= 0.68966 val_acc= 0.55738 time= 0.01563
Epoch: 0015 train_loss= 0.69325 train_acc= 0.53030 val_loss= 0.68955 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.69353 train_acc= 0.53030 val_loss= 0.68946 val_acc= 0.55738 time= 0.01563
Epoch: 0017 train_loss= 0.69362 train_acc= 0.52727 val_loss= 0.68942 val_acc= 0.55738 time= 0.00000
Epoch: 0018 train_loss= 0.69315 train_acc= 0.53030 val_loss= 0.68939 val_acc= 0.55738 time= 0.01563
Epoch: 0019 train_loss= 0.69356 train_acc= 0.53030 val_loss= 0.68941 val_acc= 0.55738 time= 0.01563
Epoch: 0020 train_loss= 0.69246 train_acc= 0.53030 val_loss= 0.68938 val_acc= 0.55738 time= 0.00000
Epoch: 0021 train_loss= 0.69362 train_acc= 0.52727 val_loss= 0.68943 val_acc= 0.55738 time= 0.01562
Epoch: 0022 train_loss= 0.69227 train_acc= 0.53030 val_loss= 0.68942 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.69287 train_acc= 0.53030 val_loss= 0.68938 val_acc= 0.55738 time= 0.00000
Epoch: 0024 train_loss= 0.69310 train_acc= 0.53030 val_loss= 0.68939 val_acc= 0.55738 time= 0.01563
Epoch: 0025 train_loss= 0.69275 train_acc= 0.53333 val_loss= 0.68936 val_acc= 0.55738 time= 0.01563
Epoch: 0026 train_loss= 0.69297 train_acc= 0.52727 val_loss= 0.68929 val_acc= 0.55738 time= 0.00000
Epoch: 0027 train_loss= 0.69265 train_acc= 0.53030 val_loss= 0.68925 val_acc= 0.55738 time= 0.01563
Epoch: 0028 train_loss= 0.69317 train_acc= 0.53030 val_loss= 0.68924 val_acc= 0.55738 time= 0.00000
Epoch: 0029 train_loss= 0.69242 train_acc= 0.53030 val_loss= 0.68920 val_acc= 0.55738 time= 0.00499
Epoch: 0030 train_loss= 0.69239 train_acc= 0.53030 val_loss= 0.68912 val_acc= 0.55738 time= 0.01100
Epoch: 0031 train_loss= 0.69254 train_acc= 0.53030 val_loss= 0.68906 val_acc= 0.55738 time= 0.00000
Epoch: 0032 train_loss= 0.69205 train_acc= 0.53030 val_loss= 0.68901 val_acc= 0.55738 time= 0.01563
Epoch: 0033 train_loss= 0.69239 train_acc= 0.53030 val_loss= 0.68892 val_acc= 0.55738 time= 0.01563
Epoch: 0034 train_loss= 0.69172 train_acc= 0.53030 val_loss= 0.68878 val_acc= 0.55738 time= 0.00000
Epoch: 0035 train_loss= 0.69264 train_acc= 0.53030 val_loss= 0.68868 val_acc= 0.55738 time= 0.01563
Epoch: 0036 train_loss= 0.69216 train_acc= 0.53030 val_loss= 0.68862 val_acc= 0.55738 time= 0.01563
Epoch: 0037 train_loss= 0.69253 train_acc= 0.53030 val_loss= 0.68859 val_acc= 0.55738 time= 0.00000
Epoch: 0038 train_loss= 0.69233 train_acc= 0.53030 val_loss= 0.68857 val_acc= 0.55738 time= 0.00000
Epoch: 0039 train_loss= 0.69211 train_acc= 0.53030 val_loss= 0.68856 val_acc= 0.55738 time= 0.01563
Epoch: 0040 train_loss= 0.69209 train_acc= 0.53030 val_loss= 0.68854 val_acc= 0.55738 time= 0.01563
Epoch: 0041 train_loss= 0.69226 train_acc= 0.53030 val_loss= 0.68855 val_acc= 0.55738 time= 0.01563
Epoch: 0042 train_loss= 0.69298 train_acc= 0.53030 val_loss= 0.68865 val_acc= 0.55738 time= 0.01563
Epoch: 0043 train_loss= 0.69233 train_acc= 0.53030 val_loss= 0.68872 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.68858 accuracy= 0.54918 time= 0.01563 
