Epoch: 0001 train_loss= 1.39165 train_acc= 0.31564 val_loss= 1.39019 val_acc= 0.28571 time= 0.34323
Epoch: 0002 train_loss= 1.39005 train_acc= 0.31564 val_loss= 1.38869 val_acc= 0.28571 time= 0.01562
Epoch: 0003 train_loss= 1.38842 train_acc= 0.31564 val_loss= 1.38702 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.38701 train_acc= 0.31564 val_loss= 1.38527 val_acc= 0.28571 time= 0.03125
Epoch: 0005 train_loss= 1.38548 train_acc= 0.31564 val_loss= 1.38350 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.38420 train_acc= 0.31564 val_loss= 1.38180 val_acc= 0.28571 time= 0.01563
Epoch: 0007 train_loss= 1.38267 train_acc= 0.31564 val_loss= 1.38014 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.38178 train_acc= 0.31564 val_loss= 1.37854 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.38043 train_acc= 0.31564 val_loss= 1.37704 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.37995 train_acc= 0.31564 val_loss= 1.37569 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.38002 train_acc= 0.31564 val_loss= 1.37454 val_acc= 0.28571 time= 0.03125
Epoch: 0012 train_loss= 1.37941 train_acc= 0.31564 val_loss= 1.37357 val_acc= 0.28571 time= 0.01563
Epoch: 0013 train_loss= 1.37902 train_acc= 0.31564 val_loss= 1.37280 val_acc= 0.28571 time= 0.01563
Epoch: 0014 train_loss= 1.37861 train_acc= 0.31564 val_loss= 1.37216 val_acc= 0.28571 time= 0.01563
Epoch: 0015 train_loss= 1.37921 train_acc= 0.31564 val_loss= 1.37161 val_acc= 0.28571 time= 0.01563
Epoch: 0016 train_loss= 1.37883 train_acc= 0.31564 val_loss= 1.37115 val_acc= 0.28571 time= 0.01563
Epoch: 0017 train_loss= 1.37943 train_acc= 0.31564 val_loss= 1.37073 val_acc= 0.28571 time= 0.03125
Epoch: 0018 train_loss= 1.37857 train_acc= 0.31564 val_loss= 1.37041 val_acc= 0.28571 time= 0.01563
Epoch: 0019 train_loss= 1.37861 train_acc= 0.31564 val_loss= 1.37017 val_acc= 0.28571 time= 0.01563
Epoch: 0020 train_loss= 1.37828 train_acc= 0.31564 val_loss= 1.37006 val_acc= 0.28571 time= 0.01563
Epoch: 0021 train_loss= 1.37816 train_acc= 0.31564 val_loss= 1.37005 val_acc= 0.28571 time= 0.01563
Epoch: 0022 train_loss= 1.37726 train_acc= 0.31564 val_loss= 1.37013 val_acc= 0.28571 time= 0.01563
Epoch: 0023 train_loss= 1.37792 train_acc= 0.31564 val_loss= 1.37035 val_acc= 0.28571 time= 0.01562
Epoch: 0024 train_loss= 1.37727 train_acc= 0.31564 val_loss= 1.37060 val_acc= 0.28571 time= 0.03125
Epoch: 0025 train_loss= 1.37706 train_acc= 0.31564 val_loss= 1.37090 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38821 accuracy= 0.28319 time= 0.00000 
