Epoch: 0001 train_loss= 1.39313 train_acc= 0.20950 val_loss= 1.39206 val_acc= 0.19643 time= 0.67192
Epoch: 0002 train_loss= 1.39175 train_acc= 0.28771 val_loss= 1.39183 val_acc= 0.19643 time= 0.01562
Epoch: 0003 train_loss= 1.39126 train_acc= 0.25140 val_loss= 1.39166 val_acc= 0.19643 time= 0.01563
Epoch: 0004 train_loss= 1.39028 train_acc= 0.24581 val_loss= 1.39136 val_acc= 0.19643 time= 0.01563
Epoch: 0005 train_loss= 1.38986 train_acc= 0.22905 val_loss= 1.39093 val_acc= 0.23214 time= 0.01563
Epoch: 0006 train_loss= 1.38932 train_acc= 0.23603 val_loss= 1.39039 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.38823 train_acc= 0.23743 val_loss= 1.38978 val_acc= 0.23214 time= 0.00000
Epoch: 0008 train_loss= 1.38729 train_acc= 0.25559 val_loss= 1.38911 val_acc= 0.23214 time= 0.01563
Epoch: 0009 train_loss= 1.38691 train_acc= 0.27793 val_loss= 1.38837 val_acc= 0.33929 time= 0.01562
Epoch: 0010 train_loss= 1.38635 train_acc= 0.28631 val_loss= 1.38756 val_acc= 0.33929 time= 0.01563
Epoch: 0011 train_loss= 1.38566 train_acc= 0.26955 val_loss= 1.38673 val_acc= 0.33929 time= 0.01563
Epoch: 0012 train_loss= 1.38445 train_acc= 0.29609 val_loss= 1.38585 val_acc= 0.33929 time= 0.01563
Epoch: 0013 train_loss= 1.38315 train_acc= 0.30866 val_loss= 1.38494 val_acc= 0.33929 time= 0.01563
Epoch: 0014 train_loss= 1.38211 train_acc= 0.30168 val_loss= 1.38400 val_acc= 0.33929 time= 0.00000
Epoch: 0015 train_loss= 1.38217 train_acc= 0.30447 val_loss= 1.38309 val_acc= 0.33929 time= 0.01562
Epoch: 0016 train_loss= 1.38126 train_acc= 0.30307 val_loss= 1.38222 val_acc= 0.33929 time= 0.01563
Epoch: 0017 train_loss= 1.37993 train_acc= 0.30307 val_loss= 1.38141 val_acc= 0.33929 time= 0.01563
Epoch: 0018 train_loss= 1.37984 train_acc= 0.30307 val_loss= 1.38063 val_acc= 0.33929 time= 0.01563
Epoch: 0019 train_loss= 1.37878 train_acc= 0.30587 val_loss= 1.37992 val_acc= 0.33929 time= 0.00000
Epoch: 0020 train_loss= 1.37852 train_acc= 0.30307 val_loss= 1.37931 val_acc= 0.33929 time= 0.01563
Epoch: 0021 train_loss= 1.37736 train_acc= 0.30447 val_loss= 1.37891 val_acc= 0.33929 time= 0.01563
Epoch: 0022 train_loss= 1.37694 train_acc= 0.30307 val_loss= 1.37880 val_acc= 0.33929 time= 0.01563
Epoch: 0023 train_loss= 1.37770 train_acc= 0.30447 val_loss= 1.37906 val_acc= 0.33929 time= 0.01563
Epoch: 0024 train_loss= 1.37805 train_acc= 0.30447 val_loss= 1.37953 val_acc= 0.33929 time= 0.01563
Epoch: 0025 train_loss= 1.37709 train_acc= 0.30447 val_loss= 1.38016 val_acc= 0.33929 time= 0.01563
Epoch: 0026 train_loss= 1.37751 train_acc= 0.30447 val_loss= 1.38070 val_acc= 0.33929 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.37832 accuracy= 0.29204 time= 0.01563 
