Epoch: 0001 train_loss= 2.12972 train_acc= 0.10782 val_loss= 2.15242 val_acc= 0.03448 time= 0.71116
Epoch: 0002 train_loss= 2.12325 train_acc= 0.10782 val_loss= 2.13993 val_acc= 0.03448 time= 0.00900
Epoch: 0003 train_loss= 2.11465 train_acc= 0.10782 val_loss= 2.12746 val_acc= 0.03448 time= 0.00900
Epoch: 0004 train_loss= 2.11371 train_acc= 0.10782 val_loss= 2.11678 val_acc= 0.03448 time= 0.00900
Epoch: 0005 train_loss= 2.10195 train_acc= 0.11321 val_loss= 2.10718 val_acc= 0.06897 time= 0.00800
Epoch: 0006 train_loss= 2.09870 train_acc= 0.10782 val_loss= 2.09914 val_acc= 0.10345 time= 0.00900
Epoch: 0007 train_loss= 2.09141 train_acc= 0.10782 val_loss= 2.09233 val_acc= 0.13793 time= 0.00900
Epoch: 0008 train_loss= 2.08227 train_acc= 0.15364 val_loss= 2.08675 val_acc= 0.10345 time= 0.00900
Epoch: 0009 train_loss= 2.08154 train_acc= 0.14555 val_loss= 2.08280 val_acc= 0.13793 time= 0.00800
Epoch: 0010 train_loss= 2.07743 train_acc= 0.13477 val_loss= 2.07944 val_acc= 0.10345 time= 0.01000
Epoch: 0011 train_loss= 2.07767 train_acc= 0.12129 val_loss= 2.07591 val_acc= 0.13793 time= 0.00900
Epoch: 0012 train_loss= 2.06990 train_acc= 0.13477 val_loss= 2.07240 val_acc= 0.13793 time= 0.00800
Epoch: 0013 train_loss= 2.06955 train_acc= 0.14016 val_loss= 2.06891 val_acc= 0.13793 time= 0.00700
Epoch: 0014 train_loss= 2.06765 train_acc= 0.13747 val_loss= 2.06485 val_acc= 0.13793 time= 0.00800
Epoch: 0015 train_loss= 2.05849 train_acc= 0.16981 val_loss= 2.06093 val_acc= 0.10345 time= 0.00800
Epoch: 0016 train_loss= 2.05927 train_acc= 0.14555 val_loss= 2.05709 val_acc= 0.13793 time= 0.00900
Epoch: 0017 train_loss= 2.06696 train_acc= 0.16981 val_loss= 2.05320 val_acc= 0.17241 time= 0.00900
Epoch: 0018 train_loss= 2.05496 train_acc= 0.15364 val_loss= 2.04928 val_acc= 0.17241 time= 0.00900
Epoch: 0019 train_loss= 2.05697 train_acc= 0.16442 val_loss= 2.04538 val_acc= 0.17241 time= 0.00900
Epoch: 0020 train_loss= 2.05301 train_acc= 0.15633 val_loss= 2.04161 val_acc= 0.17241 time= 0.00800
Epoch: 0021 train_loss= 2.05434 train_acc= 0.16173 val_loss= 2.03787 val_acc= 0.17241 time= 0.00800
Epoch: 0022 train_loss= 2.05097 train_acc= 0.15903 val_loss= 2.03436 val_acc= 0.17241 time= 0.00900
Epoch: 0023 train_loss= 2.04960 train_acc= 0.15903 val_loss= 2.03163 val_acc= 0.17241 time= 0.00800
Epoch: 0024 train_loss= 2.05009 train_acc= 0.15364 val_loss= 2.02906 val_acc= 0.13793 time= 0.00894
Epoch: 0025 train_loss= 2.04989 train_acc= 0.16981 val_loss= 2.02680 val_acc= 0.13793 time= 0.00800
Epoch: 0026 train_loss= 2.04813 train_acc= 0.16712 val_loss= 2.02536 val_acc= 0.10345 time= 0.00800
Epoch: 0027 train_loss= 2.04311 train_acc= 0.19137 val_loss= 2.02421 val_acc= 0.17241 time= 0.00900
Epoch: 0028 train_loss= 2.04728 train_acc= 0.18868 val_loss= 2.02301 val_acc= 0.17241 time= 0.00800
Epoch: 0029 train_loss= 2.04473 train_acc= 0.18059 val_loss= 2.02194 val_acc= 0.17241 time= 0.00800
Epoch: 0030 train_loss= 2.04441 train_acc= 0.17790 val_loss= 2.02083 val_acc= 0.20690 time= 0.00800
Epoch: 0031 train_loss= 2.04520 train_acc= 0.18329 val_loss= 2.02010 val_acc= 0.20690 time= 0.00800
Epoch: 0032 train_loss= 2.04395 train_acc= 0.18598 val_loss= 2.01910 val_acc= 0.20690 time= 0.00800
Epoch: 0033 train_loss= 2.04937 train_acc= 0.18059 val_loss= 2.01777 val_acc= 0.20690 time= 0.00800
Epoch: 0034 train_loss= 2.03794 train_acc= 0.17520 val_loss= 2.01680 val_acc= 0.20690 time= 0.00800
Epoch: 0035 train_loss= 2.04486 train_acc= 0.18329 val_loss= 2.01535 val_acc= 0.17241 time= 0.00900
Epoch: 0036 train_loss= 2.04242 train_acc= 0.19677 val_loss= 2.01390 val_acc= 0.17241 time= 0.00900
Epoch: 0037 train_loss= 2.03540 train_acc= 0.19407 val_loss= 2.01273 val_acc= 0.17241 time= 0.00900
Epoch: 0038 train_loss= 2.03863 train_acc= 0.17790 val_loss= 2.01167 val_acc= 0.17241 time= 0.00700
Epoch: 0039 train_loss= 2.03294 train_acc= 0.19407 val_loss= 2.01089 val_acc= 0.17241 time= 0.00900
Epoch: 0040 train_loss= 2.03780 train_acc= 0.17790 val_loss= 2.01001 val_acc= 0.17241 time= 0.00800
Epoch: 0041 train_loss= 2.03824 train_acc= 0.18598 val_loss= 2.00909 val_acc= 0.17241 time= 0.00800
Epoch: 0042 train_loss= 2.03586 train_acc= 0.18329 val_loss= 2.00781 val_acc= 0.17241 time= 0.00800
Epoch: 0043 train_loss= 2.02970 train_acc= 0.18868 val_loss= 2.00765 val_acc= 0.17241 time= 0.00900
Epoch: 0044 train_loss= 2.03755 train_acc= 0.17790 val_loss= 2.00794 val_acc= 0.17241 time= 0.01000
Epoch: 0045 train_loss= 2.03610 train_acc= 0.17790 val_loss= 2.00802 val_acc= 0.17241 time= 0.00900
Epoch: 0046 train_loss= 2.03381 train_acc= 0.17520 val_loss= 2.00781 val_acc= 0.17241 time= 0.00900
Epoch: 0047 train_loss= 2.03617 train_acc= 0.19677 val_loss= 2.00809 val_acc= 0.17241 time= 0.00800
Epoch: 0048 train_loss= 2.03535 train_acc= 0.18868 val_loss= 2.00840 val_acc= 0.17241 time= 0.00700
Epoch: 0049 train_loss= 2.03679 train_acc= 0.17520 val_loss= 2.00908 val_acc= 0.17241 time= 0.00700
Early stopping...
Optimization Finished!
Test set results: cost= 2.01223 accuracy= 0.20339 time= 0.00400 
