Epoch: 0001 train_loss= 0.69956 train_acc= 0.51455 val_loss= 0.70256 val_acc= 0.45902 time= 0.50003
Epoch: 0002 train_loss= 0.69762 train_acc= 0.52182 val_loss= 0.70182 val_acc= 0.45902 time= 0.00000
Epoch: 0003 train_loss= 0.69665 train_acc= 0.52182 val_loss= 0.70165 val_acc= 0.45902 time= 0.00000
Epoch: 0004 train_loss= 0.69695 train_acc= 0.53273 val_loss= 0.70207 val_acc= 0.45902 time= 0.01563
Epoch: 0005 train_loss= 0.69642 train_acc= 0.52545 val_loss= 0.70237 val_acc= 0.45902 time= 0.00000
Epoch: 0006 train_loss= 0.69809 train_acc= 0.51636 val_loss= 0.70179 val_acc= 0.45902 time= 0.00000
Epoch: 0007 train_loss= 0.69488 train_acc= 0.52909 val_loss= 0.70140 val_acc= 0.45902 time= 0.01563
Epoch: 0008 train_loss= 0.69667 train_acc= 0.53091 val_loss= 0.70081 val_acc= 0.45902 time= 0.00000
Epoch: 0009 train_loss= 0.69538 train_acc= 0.53455 val_loss= 0.70008 val_acc= 0.45902 time= 0.01563
Epoch: 0010 train_loss= 0.69437 train_acc= 0.53273 val_loss= 0.69948 val_acc= 0.45902 time= 0.00000
Epoch: 0011 train_loss= 0.69606 train_acc= 0.51273 val_loss= 0.69877 val_acc= 0.45902 time= 0.00000
Epoch: 0012 train_loss= 0.69440 train_acc= 0.52000 val_loss= 0.69803 val_acc= 0.45902 time= 0.01563
Epoch: 0013 train_loss= 0.69392 train_acc= 0.53455 val_loss= 0.69743 val_acc= 0.45902 time= 0.00000
Epoch: 0014 train_loss= 0.69338 train_acc= 0.54000 val_loss= 0.69700 val_acc= 0.45902 time= 0.00000
Epoch: 0015 train_loss= 0.69504 train_acc= 0.50909 val_loss= 0.69668 val_acc= 0.45902 time= 0.01563
Epoch: 0016 train_loss= 0.69423 train_acc= 0.51273 val_loss= 0.69642 val_acc= 0.45902 time= 0.00000
Epoch: 0017 train_loss= 0.69309 train_acc= 0.53455 val_loss= 0.69639 val_acc= 0.45902 time= 0.00000
Epoch: 0018 train_loss= 0.69396 train_acc= 0.52545 val_loss= 0.69651 val_acc= 0.45902 time= 0.01563
Epoch: 0019 train_loss= 0.69311 train_acc= 0.52182 val_loss= 0.69672 val_acc= 0.45902 time= 0.00000
Epoch: 0020 train_loss= 0.69373 train_acc= 0.51636 val_loss= 0.69703 val_acc= 0.45902 time= 0.00000
Epoch: 0021 train_loss= 0.69306 train_acc= 0.52727 val_loss= 0.69720 val_acc= 0.45902 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69027 accuracy= 0.54918 time= 0.00000 
