Epoch: 0001 train_loss= 1.39431 train_acc= 0.23828 val_loss= 1.39174 val_acc= 0.25000 time= 0.25001
Epoch: 0002 train_loss= 1.39153 train_acc= 0.31836 val_loss= 1.38996 val_acc= 0.25000 time= 0.01563
Epoch: 0003 train_loss= 1.38940 train_acc= 0.32617 val_loss= 1.38897 val_acc= 0.26786 time= 0.01563
Epoch: 0004 train_loss= 1.38773 train_acc= 0.33594 val_loss= 1.38853 val_acc= 0.26786 time= 0.01563
Epoch: 0005 train_loss= 1.38656 train_acc= 0.33594 val_loss= 1.38852 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.38554 train_acc= 0.33594 val_loss= 1.38893 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.38499 train_acc= 0.33594 val_loss= 1.38954 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.38453 train_acc= 0.33594 val_loss= 1.39031 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.38391 train_acc= 0.33594 val_loss= 1.39114 val_acc= 0.26786 time= 0.01562
Epoch: 0010 train_loss= 1.38361 train_acc= 0.33594 val_loss= 1.39192 val_acc= 0.26786 time= 0.01563
Epoch: 0011 train_loss= 1.38297 train_acc= 0.33594 val_loss= 1.39262 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.38282 train_acc= 0.33594 val_loss= 1.39325 val_acc= 0.26786 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38661 accuracy= 0.29204 time= 0.00000 
