Epoch: 0001 train_loss= 1.39556 train_acc= 0.19274 val_loss= 1.39354 val_acc= 0.21429 time= 0.71193
Epoch: 0002 train_loss= 1.39307 train_acc= 0.25838 val_loss= 1.39067 val_acc= 0.21429 time= 0.01700
Epoch: 0003 train_loss= 1.39091 train_acc= 0.25838 val_loss= 1.38806 val_acc= 0.41071 time= 0.01800
Epoch: 0004 train_loss= 1.38903 train_acc= 0.30168 val_loss= 1.38556 val_acc= 0.41071 time= 0.01333
Epoch: 0005 train_loss= 1.38697 train_acc= 0.30307 val_loss= 1.38349 val_acc= 0.41071 time= 0.01562
Epoch: 0006 train_loss= 1.38532 train_acc= 0.30726 val_loss= 1.38154 val_acc= 0.41071 time= 0.01563
Epoch: 0007 train_loss= 1.38446 train_acc= 0.30587 val_loss= 1.37953 val_acc= 0.41071 time= 0.00000
Epoch: 0008 train_loss= 1.38286 train_acc= 0.30587 val_loss= 1.37747 val_acc= 0.41071 time= 0.01563
Epoch: 0009 train_loss= 1.38153 train_acc= 0.30587 val_loss= 1.37542 val_acc= 0.41071 time= 0.01562
Epoch: 0010 train_loss= 1.38057 train_acc= 0.30587 val_loss= 1.37343 val_acc= 0.41071 time= 0.01563
Epoch: 0011 train_loss= 1.37883 train_acc= 0.30587 val_loss= 1.37158 val_acc= 0.41071 time= 0.01563
Epoch: 0012 train_loss= 1.37775 train_acc= 0.30587 val_loss= 1.36984 val_acc= 0.41071 time= 0.01563
Epoch: 0013 train_loss= 1.37657 train_acc= 0.30587 val_loss= 1.36826 val_acc= 0.41071 time= 0.01563
Epoch: 0014 train_loss= 1.37655 train_acc= 0.30587 val_loss= 1.36691 val_acc= 0.41071 time= 0.01563
Epoch: 0015 train_loss= 1.37580 train_acc= 0.30587 val_loss= 1.36580 val_acc= 0.41071 time= 0.01563
Epoch: 0016 train_loss= 1.37509 train_acc= 0.30587 val_loss= 1.36498 val_acc= 0.41071 time= 0.01563
Epoch: 0017 train_loss= 1.37472 train_acc= 0.30587 val_loss= 1.36440 val_acc= 0.41071 time= 0.01563
Epoch: 0018 train_loss= 1.37471 train_acc= 0.30587 val_loss= 1.36404 val_acc= 0.41071 time= 0.01562
Epoch: 0019 train_loss= 1.37437 train_acc= 0.30587 val_loss= 1.36392 val_acc= 0.41071 time= 0.01563
Epoch: 0020 train_loss= 1.37408 train_acc= 0.30587 val_loss= 1.36397 val_acc= 0.41071 time= 0.01563
Epoch: 0021 train_loss= 1.37408 train_acc= 0.30587 val_loss= 1.36414 val_acc= 0.41071 time= 0.00000
Epoch: 0022 train_loss= 1.37467 train_acc= 0.30587 val_loss= 1.36435 val_acc= 0.41071 time= 0.01563
Epoch: 0023 train_loss= 1.37482 train_acc= 0.30587 val_loss= 1.36457 val_acc= 0.41071 time= 0.01563
Epoch: 0024 train_loss= 1.37360 train_acc= 0.30587 val_loss= 1.36479 val_acc= 0.41071 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.39532 accuracy= 0.31858 time= 0.01563 
