Epoch: 0001 train_loss= 1.39251 train_acc= 0.22905 val_loss= 1.39133 val_acc= 0.26786 time= 0.32757
Epoch: 0002 train_loss= 1.39106 train_acc= 0.22905 val_loss= 1.39210 val_acc= 0.26786 time= 0.01562
Epoch: 0003 train_loss= 1.38966 train_acc= 0.23184 val_loss= 1.39317 val_acc= 0.14286 time= 0.01563
Epoch: 0004 train_loss= 1.38823 train_acc= 0.27514 val_loss= 1.39431 val_acc= 0.14286 time= 0.01563
Epoch: 0005 train_loss= 1.38675 train_acc= 0.28073 val_loss= 1.39560 val_acc= 0.14286 time= 0.01563
Epoch: 0006 train_loss= 1.38583 train_acc= 0.26816 val_loss= 1.39709 val_acc= 0.14286 time= 0.01563
Epoch: 0007 train_loss= 1.38483 train_acc= 0.27793 val_loss= 1.39856 val_acc= 0.14286 time= 0.01563
Epoch: 0008 train_loss= 1.38406 train_acc= 0.27793 val_loss= 1.40005 val_acc= 0.14286 time= 0.01563
Epoch: 0009 train_loss= 1.38309 train_acc= 0.30028 val_loss= 1.40161 val_acc= 0.28571 time= 0.01563
Epoch: 0010 train_loss= 1.38272 train_acc= 0.27374 val_loss= 1.40306 val_acc= 0.28571 time= 0.01563
Epoch: 0011 train_loss= 1.38213 train_acc= 0.28212 val_loss= 1.40426 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.38252 train_acc= 0.30168 val_loss= 1.40489 val_acc= 0.28571 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37479 accuracy= 0.29204 time= 0.01563 
