Epoch: 0001 train_loss= 2.08522 train_acc= 0.16712 val_loss= 2.08587 val_acc= 0.13793 time= 0.80045
Epoch: 0002 train_loss= 2.08261 train_acc= 0.17790 val_loss= 2.08671 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08065 train_acc= 0.17790 val_loss= 2.08774 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.07759 train_acc= 0.18598 val_loss= 2.08896 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07627 train_acc= 0.18059 val_loss= 2.09037 val_acc= 0.13793 time= 0.00000
Epoch: 0006 train_loss= 2.07236 train_acc= 0.18598 val_loss= 2.09206 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07113 train_acc= 0.18059 val_loss= 2.09393 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.06911 train_acc= 0.18868 val_loss= 2.09609 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.06605 train_acc= 0.18329 val_loss= 2.09847 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06532 train_acc= 0.18059 val_loss= 2.10103 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.06146 train_acc= 0.18329 val_loss= 2.10380 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.06316 train_acc= 0.19137 val_loss= 2.10622 val_acc= 0.13793 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.10674 accuracy= 0.13559 time= 0.00000 
