Epoch: 0001 train_loss= 2.10703 train_acc= 0.13208 val_loss= 2.03594 val_acc= 0.20690 time= 0.95544
Epoch: 0002 train_loss= 2.09795 train_acc= 0.13477 val_loss= 2.03230 val_acc= 0.20690 time= 0.01563
Epoch: 0003 train_loss= 2.09448 train_acc= 0.14825 val_loss= 2.03235 val_acc= 0.20690 time= 0.00000
Epoch: 0004 train_loss= 2.10304 train_acc= 0.12399 val_loss= 2.03971 val_acc= 0.20690 time= 0.01563
Epoch: 0005 train_loss= 2.08143 train_acc= 0.15633 val_loss= 2.04389 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.08862 train_acc= 0.14286 val_loss= 2.04315 val_acc= 0.24138 time= 0.01563
Epoch: 0007 train_loss= 2.07529 train_acc= 0.13477 val_loss= 2.04573 val_acc= 0.24138 time= 0.01563
Epoch: 0008 train_loss= 2.09398 train_acc= 0.14286 val_loss= 2.04840 val_acc= 0.24138 time= 0.01563
Epoch: 0009 train_loss= 2.08356 train_acc= 0.12129 val_loss= 2.05157 val_acc= 0.20690 time= 0.01563
Epoch: 0010 train_loss= 2.06982 train_acc= 0.15903 val_loss= 2.05528 val_acc= 0.20690 time= 0.01563
Epoch: 0011 train_loss= 2.07404 train_acc= 0.15094 val_loss= 2.05838 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.06774 train_acc= 0.12399 val_loss= 2.06053 val_acc= 0.13793 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 2.10813 accuracy= 0.08475 time= 0.01563 
