Epoch: 0001 train_loss= 0.69990 train_acc= 0.50303 val_loss= 0.69849 val_acc= 0.60656 time= 0.09323
Epoch: 0002 train_loss= 0.69881 train_acc= 0.51212 val_loss= 0.69578 val_acc= 0.60656 time= 0.00000
Epoch: 0003 train_loss= 0.69819 train_acc= 0.48485 val_loss= 0.69348 val_acc= 0.60656 time= 0.01563
Epoch: 0004 train_loss= 0.69763 train_acc= 0.49394 val_loss= 0.69171 val_acc= 0.60656 time= 0.01563
Epoch: 0005 train_loss= 0.69725 train_acc= 0.49394 val_loss= 0.69061 val_acc= 0.60656 time= 0.01563
Epoch: 0006 train_loss= 0.69670 train_acc= 0.49394 val_loss= 0.68970 val_acc= 0.60656 time= 0.00000
Epoch: 0007 train_loss= 0.69632 train_acc= 0.49394 val_loss= 0.68889 val_acc= 0.60656 time= 0.01563
Epoch: 0008 train_loss= 0.69604 train_acc= 0.49394 val_loss= 0.68817 val_acc= 0.60656 time= 0.01563
Epoch: 0009 train_loss= 0.69591 train_acc= 0.49394 val_loss= 0.68765 val_acc= 0.60656 time= 0.01563
Epoch: 0010 train_loss= 0.69526 train_acc= 0.49394 val_loss= 0.68722 val_acc= 0.60656 time= 0.01563
Epoch: 0011 train_loss= 0.69514 train_acc= 0.49394 val_loss= 0.68703 val_acc= 0.60656 time= 0.00000
Epoch: 0012 train_loss= 0.69465 train_acc= 0.49394 val_loss= 0.68684 val_acc= 0.60656 time= 0.01563
Epoch: 0013 train_loss= 0.69471 train_acc= 0.49394 val_loss= 0.68671 val_acc= 0.60656 time= 0.01563
Epoch: 0014 train_loss= 0.69450 train_acc= 0.49394 val_loss= 0.68675 val_acc= 0.60656 time= 0.01563
Epoch: 0015 train_loss= 0.69456 train_acc= 0.49394 val_loss= 0.68686 val_acc= 0.60656 time= 0.00000
Epoch: 0016 train_loss= 0.69409 train_acc= 0.49394 val_loss= 0.68700 val_acc= 0.60656 time= 0.01563
Epoch: 0017 train_loss= 0.69361 train_acc= 0.49394 val_loss= 0.68699 val_acc= 0.60656 time= 0.01563
Epoch: 0018 train_loss= 0.69362 train_acc= 0.49394 val_loss= 0.68694 val_acc= 0.60656 time= 0.01563
Epoch: 0019 train_loss= 0.69346 train_acc= 0.49394 val_loss= 0.68695 val_acc= 0.60656 time= 0.01563
Epoch: 0020 train_loss= 0.69319 train_acc= 0.49394 val_loss= 0.68683 val_acc= 0.60656 time= 0.00000
Epoch: 0021 train_loss= 0.69320 train_acc= 0.49394 val_loss= 0.68661 val_acc= 0.60656 time= 0.01563
Epoch: 0022 train_loss= 0.69310 train_acc= 0.49394 val_loss= 0.68634 val_acc= 0.60656 time= 0.01562
Epoch: 0023 train_loss= 0.69302 train_acc= 0.49394 val_loss= 0.68617 val_acc= 0.60656 time= 0.01563
Epoch: 0024 train_loss= 0.69307 train_acc= 0.49394 val_loss= 0.68604 val_acc= 0.60656 time= 0.01563
Epoch: 0025 train_loss= 0.69280 train_acc= 0.49394 val_loss= 0.68588 val_acc= 0.60656 time= 0.00000
Epoch: 0026 train_loss= 0.69260 train_acc= 0.49394 val_loss= 0.68566 val_acc= 0.60656 time= 0.01563
Epoch: 0027 train_loss= 0.69293 train_acc= 0.49394 val_loss= 0.68555 val_acc= 0.60656 time= 0.01563
Epoch: 0028 train_loss= 0.69254 train_acc= 0.49394 val_loss= 0.68533 val_acc= 0.60656 time= 0.01563
Epoch: 0029 train_loss= 0.69301 train_acc= 0.49394 val_loss= 0.68521 val_acc= 0.60656 time= 0.01563
Epoch: 0030 train_loss= 0.69261 train_acc= 0.49394 val_loss= 0.68508 val_acc= 0.60656 time= 0.00000
Epoch: 0031 train_loss= 0.69285 train_acc= 0.49394 val_loss= 0.68508 val_acc= 0.60656 time= 0.01562
Epoch: 0032 train_loss= 0.69254 train_acc= 0.49394 val_loss= 0.68511 val_acc= 0.60656 time= 0.01563
Epoch: 0033 train_loss= 0.69273 train_acc= 0.49394 val_loss= 0.68524 val_acc= 0.60656 time= 0.01563
Epoch: 0034 train_loss= 0.69267 train_acc= 0.49394 val_loss= 0.68537 val_acc= 0.60656 time= 0.01563
Epoch: 0035 train_loss= 0.69270 train_acc= 0.49394 val_loss= 0.68556 val_acc= 0.60656 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69907 accuracy= 0.44262 time= 0.01563 
