Epoch: 0001 train_loss= 2.08391 train_acc= 0.12453 val_loss= 2.08044 val_acc= 0.06897 time= 0.34406
Epoch: 0002 train_loss= 2.08235 train_acc= 0.13962 val_loss= 2.07893 val_acc= 0.17241 time= 0.01563
Epoch: 0003 train_loss= 2.08062 train_acc= 0.15472 val_loss= 2.07758 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.07928 train_acc= 0.14717 val_loss= 2.07624 val_acc= 0.17241 time= 0.01563
Epoch: 0005 train_loss= 2.07804 train_acc= 0.16604 val_loss= 2.07498 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.07510 train_acc= 0.18491 val_loss= 2.07383 val_acc= 0.17241 time= 0.01563
Epoch: 0007 train_loss= 2.07412 train_acc= 0.18491 val_loss= 2.07278 val_acc= 0.17241 time= 0.00000
Epoch: 0008 train_loss= 2.07347 train_acc= 0.15849 val_loss= 2.07188 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07133 train_acc= 0.15472 val_loss= 2.07122 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.06963 train_acc= 0.13208 val_loss= 2.07078 val_acc= 0.13793 time= 0.01562
Epoch: 0011 train_loss= 2.06746 train_acc= 0.15094 val_loss= 2.07063 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.06574 train_acc= 0.14340 val_loss= 2.07087 val_acc= 0.13793 time= 0.01563
Epoch: 0013 train_loss= 2.06677 train_acc= 0.14717 val_loss= 2.07149 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.06318 train_acc= 0.13962 val_loss= 2.07254 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07419 accuracy= 0.13559 time= 0.00000 
