Epoch: 0001 train_loss= 1.37709 train_acc= 0.33398 val_loss= 1.38287 val_acc= 0.28571 time= 0.53129
Epoch: 0002 train_loss= 1.37726 train_acc= 0.33398 val_loss= 1.38137 val_acc= 0.28571 time= 0.00000
Epoch: 0003 train_loss= 1.37567 train_acc= 0.33398 val_loss= 1.37995 val_acc= 0.28571 time= 0.01563
Epoch: 0004 train_loss= 1.37600 train_acc= 0.33008 val_loss= 1.37866 val_acc= 0.28571 time= 0.00000
Epoch: 0005 train_loss= 1.37409 train_acc= 0.33398 val_loss= 1.37753 val_acc= 0.28571 time= 0.01563
Epoch: 0006 train_loss= 1.37163 train_acc= 0.33203 val_loss= 1.37651 val_acc= 0.28571 time= 0.00000
Epoch: 0007 train_loss= 1.37270 train_acc= 0.33398 val_loss= 1.37558 val_acc= 0.28571 time= 0.01563
Epoch: 0008 train_loss= 1.37172 train_acc= 0.33008 val_loss= 1.37480 val_acc= 0.28571 time= 0.00000
Epoch: 0009 train_loss= 1.37019 train_acc= 0.33203 val_loss= 1.37420 val_acc= 0.28571 time= 0.00000
Epoch: 0010 train_loss= 1.37064 train_acc= 0.33203 val_loss= 1.37377 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.37035 train_acc= 0.33203 val_loss= 1.37345 val_acc= 0.28571 time= 0.00000
Epoch: 0012 train_loss= 1.36973 train_acc= 0.33203 val_loss= 1.37331 val_acc= 0.28571 time= 0.01563
Epoch: 0013 train_loss= 1.37016 train_acc= 0.33203 val_loss= 1.37331 val_acc= 0.28571 time= 0.00000
Epoch: 0014 train_loss= 1.37019 train_acc= 0.33203 val_loss= 1.37347 val_acc= 0.28571 time= 0.01563
Epoch: 0015 train_loss= 1.37131 train_acc= 0.33203 val_loss= 1.37369 val_acc= 0.28571 time= 0.00000
Epoch: 0016 train_loss= 1.37154 train_acc= 0.33203 val_loss= 1.37391 val_acc= 0.28571 time= 0.00000
Epoch: 0017 train_loss= 1.37086 train_acc= 0.33203 val_loss= 1.37412 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.39557 accuracy= 0.28319 time= 0.00000 
