Epoch: 0001 train_loss= 2.08739 train_acc= 0.09434 val_loss= 2.08596 val_acc= 0.03448 time= 0.15627
Epoch: 0002 train_loss= 2.08475 train_acc= 0.18239 val_loss= 2.08556 val_acc= 0.03448 time= 0.00000
Epoch: 0003 train_loss= 2.08235 train_acc= 0.18868 val_loss= 2.08559 val_acc= 0.03448 time= 0.01562
Epoch: 0004 train_loss= 2.08070 train_acc= 0.18868 val_loss= 2.08617 val_acc= 0.03448 time= 0.00000
Epoch: 0005 train_loss= 2.07866 train_acc= 0.18868 val_loss= 2.08747 val_acc= 0.03448 time= 0.01563
Epoch: 0006 train_loss= 2.07660 train_acc= 0.18868 val_loss= 2.08936 val_acc= 0.03448 time= 0.00000
Epoch: 0007 train_loss= 2.07546 train_acc= 0.18868 val_loss= 2.09185 val_acc= 0.03448 time= 0.01563
Epoch: 0008 train_loss= 2.07380 train_acc= 0.18868 val_loss= 2.09467 val_acc= 0.03448 time= 0.00000
Epoch: 0009 train_loss= 2.07266 train_acc= 0.18868 val_loss= 2.09783 val_acc= 0.03448 time= 0.00000
Epoch: 0010 train_loss= 2.07083 train_acc= 0.18868 val_loss= 2.10136 val_acc= 0.03448 time= 0.01563
Epoch: 0011 train_loss= 2.07017 train_acc= 0.18868 val_loss= 2.10512 val_acc= 0.03448 time= 0.00000
Epoch: 0012 train_loss= 2.06856 train_acc= 0.18868 val_loss= 2.10917 val_acc= 0.03448 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07275 accuracy= 0.18644 time= 0.00000 
