Epoch: 0001 train_loss= 1.38983 train_acc= 0.24302 val_loss= 1.39904 val_acc= 0.23333 time= 0.81259
Epoch: 0002 train_loss= 1.38726 train_acc= 0.26257 val_loss= 1.39703 val_acc= 0.21667 time= 0.01563
Epoch: 0003 train_loss= 1.38426 train_acc= 0.30587 val_loss= 1.39543 val_acc= 0.21667 time= 0.00000
Epoch: 0004 train_loss= 1.38487 train_acc= 0.28911 val_loss= 1.39423 val_acc= 0.21667 time= 0.01563
Epoch: 0005 train_loss= 1.38170 train_acc= 0.30168 val_loss= 1.39345 val_acc= 0.21667 time= 0.00000
Epoch: 0006 train_loss= 1.38001 train_acc= 0.30447 val_loss= 1.39303 val_acc= 0.21667 time= 0.00000
Epoch: 0007 train_loss= 1.37986 train_acc= 0.30307 val_loss= 1.39293 val_acc= 0.21667 time= 0.01563
Epoch: 0008 train_loss= 1.38023 train_acc= 0.30307 val_loss= 1.39294 val_acc= 0.21667 time= 0.00000
Epoch: 0009 train_loss= 1.37898 train_acc= 0.30307 val_loss= 1.39298 val_acc= 0.21667 time= 0.01563
Epoch: 0010 train_loss= 1.37827 train_acc= 0.30307 val_loss= 1.39293 val_acc= 0.21667 time= 0.00000
Epoch: 0011 train_loss= 1.37997 train_acc= 0.30307 val_loss= 1.39269 val_acc= 0.21667 time= 0.01563
Epoch: 0012 train_loss= 1.37944 train_acc= 0.30307 val_loss= 1.39232 val_acc= 0.21667 time= 0.00000
Epoch: 0013 train_loss= 1.37929 train_acc= 0.30307 val_loss= 1.39193 val_acc= 0.21667 time= 0.01562
Epoch: 0014 train_loss= 1.37841 train_acc= 0.30307 val_loss= 1.39152 val_acc= 0.21667 time= 0.00000
Epoch: 0015 train_loss= 1.37816 train_acc= 0.30307 val_loss= 1.39117 val_acc= 0.21667 time= 0.01563
Epoch: 0016 train_loss= 1.37828 train_acc= 0.30447 val_loss= 1.39090 val_acc= 0.21667 time= 0.00000
Epoch: 0017 train_loss= 1.37719 train_acc= 0.30307 val_loss= 1.39071 val_acc= 0.21667 time= 0.01563
Epoch: 0018 train_loss= 1.37679 train_acc= 0.30168 val_loss= 1.39063 val_acc= 0.21667 time= 0.00000
Epoch: 0019 train_loss= 1.37625 train_acc= 0.30307 val_loss= 1.39058 val_acc= 0.21667 time= 0.01563
Epoch: 0020 train_loss= 1.37712 train_acc= 0.30168 val_loss= 1.39049 val_acc= 0.21667 time= 0.00000
Epoch: 0021 train_loss= 1.37789 train_acc= 0.30447 val_loss= 1.39032 val_acc= 0.21667 time= 0.01563
Epoch: 0022 train_loss= 1.37764 train_acc= 0.30447 val_loss= 1.39012 val_acc= 0.21667 time= 0.00000
Epoch: 0023 train_loss= 1.37685 train_acc= 0.30587 val_loss= 1.38984 val_acc= 0.21667 time= 0.01563
Epoch: 0024 train_loss= 1.37720 train_acc= 0.30307 val_loss= 1.38955 val_acc= 0.21667 time= 0.00000
Epoch: 0025 train_loss= 1.37675 train_acc= 0.30168 val_loss= 1.38933 val_acc= 0.21667 time= 0.01563
Epoch: 0026 train_loss= 1.37729 train_acc= 0.30447 val_loss= 1.38915 val_acc= 0.21667 time= 0.00000
Epoch: 0027 train_loss= 1.37769 train_acc= 0.30168 val_loss= 1.38902 val_acc= 0.21667 time= 0.01563
Epoch: 0028 train_loss= 1.37635 train_acc= 0.30307 val_loss= 1.38903 val_acc= 0.21667 time= 0.00000
Epoch: 0029 train_loss= 1.37690 train_acc= 0.30307 val_loss= 1.38913 val_acc= 0.21667 time= 0.01563
Epoch: 0030 train_loss= 1.37627 train_acc= 0.30307 val_loss= 1.38937 val_acc= 0.21667 time= 0.00000
Epoch: 0031 train_loss= 1.37762 train_acc= 0.30307 val_loss= 1.38958 val_acc= 0.21667 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37225 accuracy= 0.31667 time= 0.00000 
