Epoch: 0001 train_loss= 2.08714 train_acc= 0.11321 val_loss= 2.08468 val_acc= 0.20690 time= 0.23439
Epoch: 0002 train_loss= 2.08455 train_acc= 0.16604 val_loss= 2.08255 val_acc= 0.20690 time= 0.00000
Epoch: 0003 train_loss= 2.08209 train_acc= 0.16604 val_loss= 2.08054 val_acc= 0.20690 time= 0.01563
Epoch: 0004 train_loss= 2.07989 train_acc= 0.16604 val_loss= 2.07878 val_acc= 0.20690 time= 0.01563
Epoch: 0005 train_loss= 2.07828 train_acc= 0.16604 val_loss= 2.07727 val_acc= 0.20690 time= 0.00000
Epoch: 0006 train_loss= 2.07614 train_acc= 0.16604 val_loss= 2.07599 val_acc= 0.20690 time= 0.01563
Epoch: 0007 train_loss= 2.07509 train_acc= 0.16604 val_loss= 2.07483 val_acc= 0.20690 time= 0.00000
Epoch: 0008 train_loss= 2.07346 train_acc= 0.16604 val_loss= 2.07383 val_acc= 0.20690 time= 0.01563
Epoch: 0009 train_loss= 2.07251 train_acc= 0.16604 val_loss= 2.07297 val_acc= 0.20690 time= 0.01562
Epoch: 0010 train_loss= 2.07133 train_acc= 0.16604 val_loss= 2.07214 val_acc= 0.20690 time= 0.00000
Epoch: 0011 train_loss= 2.07004 train_acc= 0.16604 val_loss= 2.07127 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.06934 train_acc= 0.16604 val_loss= 2.07048 val_acc= 0.20690 time= 0.01563
Epoch: 0013 train_loss= 2.06885 train_acc= 0.16604 val_loss= 2.06968 val_acc= 0.20690 time= 0.00195
Epoch: 0014 train_loss= 2.06728 train_acc= 0.16604 val_loss= 2.06889 val_acc= 0.20690 time= 0.01563
Epoch: 0015 train_loss= 2.06662 train_acc= 0.16604 val_loss= 2.06798 val_acc= 0.20690 time= 0.01080
Epoch: 0016 train_loss= 2.06605 train_acc= 0.16604 val_loss= 2.06702 val_acc= 0.20690 time= 0.00000
Epoch: 0017 train_loss= 2.06509 train_acc= 0.16604 val_loss= 2.06614 val_acc= 0.20690 time= 0.01565
Epoch: 0018 train_loss= 2.06420 train_acc= 0.16604 val_loss= 2.06530 val_acc= 0.20690 time= 0.01563
Epoch: 0019 train_loss= 2.06396 train_acc= 0.16604 val_loss= 2.06462 val_acc= 0.20690 time= 0.00000
Epoch: 0020 train_loss= 2.06315 train_acc= 0.16604 val_loss= 2.06416 val_acc= 0.20690 time= 0.01563
Epoch: 0021 train_loss= 2.06256 train_acc= 0.16604 val_loss= 2.06393 val_acc= 0.20690 time= 0.00000
Epoch: 0022 train_loss= 2.06209 train_acc= 0.16604 val_loss= 2.06383 val_acc= 0.20690 time= 0.01563
Epoch: 0023 train_loss= 2.06024 train_acc= 0.16604 val_loss= 2.06386 val_acc= 0.20690 time= 0.01563
Epoch: 0024 train_loss= 2.06124 train_acc= 0.16604 val_loss= 2.06410 val_acc= 0.20690 time= 0.00000
Epoch: 0025 train_loss= 2.06027 train_acc= 0.16604 val_loss= 2.06428 val_acc= 0.20690 time= 0.01563
Epoch: 0026 train_loss= 2.06032 train_acc= 0.16604 val_loss= 2.06444 val_acc= 0.20690 time= 0.01563
Epoch: 0027 train_loss= 2.05928 train_acc= 0.16604 val_loss= 2.06462 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.03718 accuracy= 0.11864 time= 0.00000 
