Epoch: 0001 train_loss= 1.38547 train_acc= 0.32812 val_loss= 1.39031 val_acc= 0.32143 time= 0.48441
Epoch: 0002 train_loss= 1.38097 train_acc= 0.32031 val_loss= 1.38818 val_acc= 0.32143 time= 0.01562
Epoch: 0003 train_loss= 1.37949 train_acc= 0.32617 val_loss= 1.38636 val_acc= 0.32143 time= 0.00000
Epoch: 0004 train_loss= 1.37733 train_acc= 0.32422 val_loss= 1.38484 val_acc= 0.32143 time= 0.00000
Epoch: 0005 train_loss= 1.37499 train_acc= 0.32422 val_loss= 1.38364 val_acc= 0.32143 time= 0.01563
Epoch: 0006 train_loss= 1.37334 train_acc= 0.32617 val_loss= 1.38275 val_acc= 0.32143 time= 0.00000
Epoch: 0007 train_loss= 1.37232 train_acc= 0.32617 val_loss= 1.38209 val_acc= 0.32143 time= 0.01563
Epoch: 0008 train_loss= 1.37174 train_acc= 0.32617 val_loss= 1.38174 val_acc= 0.32143 time= 0.00000
Epoch: 0009 train_loss= 1.37190 train_acc= 0.32617 val_loss= 1.38143 val_acc= 0.32143 time= 0.01563
Epoch: 0010 train_loss= 1.37059 train_acc= 0.32617 val_loss= 1.38119 val_acc= 0.32143 time= 0.00000
Epoch: 0011 train_loss= 1.36855 train_acc= 0.32617 val_loss= 1.38099 val_acc= 0.32143 time= 0.00000
Epoch: 0012 train_loss= 1.36996 train_acc= 0.32422 val_loss= 1.38079 val_acc= 0.32143 time= 0.01563
Epoch: 0013 train_loss= 1.37133 train_acc= 0.32617 val_loss= 1.38038 val_acc= 0.32143 time= 0.00000
Epoch: 0014 train_loss= 1.37007 train_acc= 0.32617 val_loss= 1.37998 val_acc= 0.32143 time= 0.01563
Epoch: 0015 train_loss= 1.36859 train_acc= 0.32812 val_loss= 1.37964 val_acc= 0.32143 time= 0.00000
Epoch: 0016 train_loss= 1.36867 train_acc= 0.32617 val_loss= 1.37935 val_acc= 0.32143 time= 0.01563
Epoch: 0017 train_loss= 1.37001 train_acc= 0.32617 val_loss= 1.37909 val_acc= 0.32143 time= 0.00000
Epoch: 0018 train_loss= 1.36964 train_acc= 0.32617 val_loss= 1.37891 val_acc= 0.32143 time= 0.01563
Epoch: 0019 train_loss= 1.36932 train_acc= 0.32617 val_loss= 1.37885 val_acc= 0.32143 time= 0.00000
Epoch: 0020 train_loss= 1.36866 train_acc= 0.32617 val_loss= 1.37892 val_acc= 0.32143 time= 0.00000
Epoch: 0021 train_loss= 1.36913 train_acc= 0.32422 val_loss= 1.37918 val_acc= 0.32143 time= 0.01562
Epoch: 0022 train_loss= 1.36757 train_acc= 0.32617 val_loss= 1.37916 val_acc= 0.32143 time= 0.00000
Epoch: 0023 train_loss= 1.36608 train_acc= 0.32617 val_loss= 1.37925 val_acc= 0.32143 time= 0.01563
Epoch: 0024 train_loss= 1.36926 train_acc= 0.32617 val_loss= 1.37930 val_acc= 0.32143 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.38096 accuracy= 0.29204 time= 0.00000 
