Epoch: 0001 train_loss= 0.69796 train_acc= 0.49394 val_loss= 0.69173 val_acc= 0.60656 time= 0.10987
Epoch: 0002 train_loss= 0.69749 train_acc= 0.49394 val_loss= 0.69020 val_acc= 0.60656 time= 0.01563
Epoch: 0003 train_loss= 0.69711 train_acc= 0.49394 val_loss= 0.68920 val_acc= 0.60656 time= 0.00000
Epoch: 0004 train_loss= 0.69697 train_acc= 0.49394 val_loss= 0.68864 val_acc= 0.60656 time= 0.01563
Epoch: 0005 train_loss= 0.69638 train_acc= 0.49394 val_loss= 0.68812 val_acc= 0.60656 time= 0.01563
Epoch: 0006 train_loss= 0.69616 train_acc= 0.49394 val_loss= 0.68786 val_acc= 0.60656 time= 0.00000
Epoch: 0007 train_loss= 0.69534 train_acc= 0.49394 val_loss= 0.68750 val_acc= 0.60656 time= 0.01563
Epoch: 0008 train_loss= 0.69502 train_acc= 0.49394 val_loss= 0.68712 val_acc= 0.60656 time= 0.00000
Epoch: 0009 train_loss= 0.69473 train_acc= 0.49394 val_loss= 0.68676 val_acc= 0.60656 time= 0.01563
Epoch: 0010 train_loss= 0.69535 train_acc= 0.49394 val_loss= 0.68674 val_acc= 0.60656 time= 0.01563
Epoch: 0011 train_loss= 0.69482 train_acc= 0.49394 val_loss= 0.68687 val_acc= 0.60656 time= 0.01563
Epoch: 0012 train_loss= 0.69461 train_acc= 0.49394 val_loss= 0.68715 val_acc= 0.60656 time= 0.00000
Epoch: 0013 train_loss= 0.69424 train_acc= 0.49394 val_loss= 0.68739 val_acc= 0.60656 time= 0.01563
Epoch: 0014 train_loss= 0.69377 train_acc= 0.49394 val_loss= 0.68750 val_acc= 0.60656 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69930 accuracy= 0.44262 time= 0.00000 
