Epoch: 0001 train_loss= 2.08753 train_acc= 0.15094 val_loss= 2.08592 val_acc= 0.00000 time= 0.13360
Epoch: 0002 train_loss= 2.08515 train_acc= 0.15723 val_loss= 2.08445 val_acc= 0.03448 time= 0.00000
Epoch: 0003 train_loss= 2.08326 train_acc= 0.15723 val_loss= 2.08334 val_acc= 0.10345 time= 0.01519
Epoch: 0004 train_loss= 2.08148 train_acc= 0.15094 val_loss= 2.08262 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07993 train_acc= 0.15723 val_loss= 2.08212 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07846 train_acc= 0.15723 val_loss= 2.08195 val_acc= 0.10345 time= 0.01563
Epoch: 0007 train_loss= 2.07733 train_acc= 0.15723 val_loss= 2.08226 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.07643 train_acc= 0.15723 val_loss= 2.08285 val_acc= 0.10345 time= 0.01563
Epoch: 0009 train_loss= 2.07589 train_acc= 0.15723 val_loss= 2.08366 val_acc= 0.10345 time= 0.00000
Epoch: 0010 train_loss= 2.07498 train_acc= 0.15723 val_loss= 2.08476 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.07392 train_acc= 0.16352 val_loss= 2.08634 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.07387 train_acc= 0.14465 val_loss= 2.08809 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07965 accuracy= 0.05085 time= 0.00000 
