Epoch: 0001 train_loss= 1.39125 train_acc= 0.32899 val_loss= 1.38386 val_acc= 0.44643 time= 0.12501
Epoch: 0002 train_loss= 1.38938 train_acc= 0.32899 val_loss= 1.38056 val_acc= 0.44643 time= 0.01563
Epoch: 0003 train_loss= 1.38809 train_acc= 0.33225 val_loss= 1.37720 val_acc= 0.44643 time= 0.01563
Epoch: 0004 train_loss= 1.38692 train_acc= 0.33225 val_loss= 1.37372 val_acc= 0.44643 time= 0.01563
Epoch: 0005 train_loss= 1.38492 train_acc= 0.33550 val_loss= 1.37009 val_acc= 0.44643 time= 0.01563
Epoch: 0006 train_loss= 1.38385 train_acc= 0.33225 val_loss= 1.36636 val_acc= 0.44643 time= 0.01563
Epoch: 0007 train_loss= 1.38287 train_acc= 0.33225 val_loss= 1.36254 val_acc= 0.44643 time= 0.00000
Epoch: 0008 train_loss= 1.38201 train_acc= 0.32899 val_loss= 1.35863 val_acc= 0.44643 time= 0.01563
Epoch: 0009 train_loss= 1.38038 train_acc= 0.33225 val_loss= 1.35458 val_acc= 0.44643 time= 0.01562
Epoch: 0010 train_loss= 1.37880 train_acc= 0.33225 val_loss= 1.35049 val_acc= 0.44643 time= 0.01563
Epoch: 0011 train_loss= 1.37838 train_acc= 0.33225 val_loss= 1.34613 val_acc= 0.44643 time= 0.01563
Epoch: 0012 train_loss= 1.37557 train_acc= 0.33225 val_loss= 1.34146 val_acc= 0.44643 time= 0.01563
Epoch: 0013 train_loss= 1.37376 train_acc= 0.33225 val_loss= 1.33678 val_acc= 0.44643 time= 0.00000
Epoch: 0014 train_loss= 1.37444 train_acc= 0.33225 val_loss= 1.33237 val_acc= 0.44643 time= 0.01563
Epoch: 0015 train_loss= 1.37472 train_acc= 0.32899 val_loss= 1.32853 val_acc= 0.44643 time= 0.01563
Epoch: 0016 train_loss= 1.37250 train_acc= 0.33225 val_loss= 1.32532 val_acc= 0.44643 time= 0.01563
Epoch: 0017 train_loss= 1.37387 train_acc= 0.33225 val_loss= 1.32311 val_acc= 0.44643 time= 0.01563
Epoch: 0018 train_loss= 1.37358 train_acc= 0.32899 val_loss= 1.32206 val_acc= 0.44643 time= 0.00000
Epoch: 0019 train_loss= 1.37406 train_acc= 0.32899 val_loss= 1.32166 val_acc= 0.44643 time= 0.01563
Epoch: 0020 train_loss= 1.37342 train_acc= 0.33225 val_loss= 1.32205 val_acc= 0.44643 time= 0.01563
Epoch: 0021 train_loss= 1.37168 train_acc= 0.33225 val_loss= 1.32292 val_acc= 0.44643 time= 0.01563
Epoch: 0022 train_loss= 1.37094 train_acc= 0.33225 val_loss= 1.32419 val_acc= 0.44643 time= 0.01562
Epoch: 0023 train_loss= 1.37281 train_acc= 0.33225 val_loss= 1.32569 val_acc= 0.44643 time= 0.00000
Epoch: 0024 train_loss= 1.37205 train_acc= 0.33225 val_loss= 1.32735 val_acc= 0.44643 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38463 accuracy= 0.29204 time= 0.01563 
