Epoch: 0001 train_loss= 0.69939 train_acc= 0.48831 val_loss= 0.69802 val_acc= 0.58065 time= 0.34377
Epoch: 0002 train_loss= 0.69859 train_acc= 0.51818 val_loss= 0.69666 val_acc= 0.58065 time= 0.01563
Epoch: 0003 train_loss= 0.69814 train_acc= 0.51299 val_loss= 0.69574 val_acc= 0.58065 time= 0.02400
Epoch: 0004 train_loss= 0.69756 train_acc= 0.51299 val_loss= 0.69508 val_acc= 0.58065 time= 0.01500
Epoch: 0005 train_loss= 0.69715 train_acc= 0.51299 val_loss= 0.69452 val_acc= 0.58065 time= 0.00610
Epoch: 0006 train_loss= 0.69684 train_acc= 0.51299 val_loss= 0.69412 val_acc= 0.58065 time= 0.01563
Epoch: 0007 train_loss= 0.69638 train_acc= 0.51299 val_loss= 0.69383 val_acc= 0.58065 time= 0.01563
Epoch: 0008 train_loss= 0.69588 train_acc= 0.51299 val_loss= 0.69359 val_acc= 0.58065 time= 0.01563
Epoch: 0009 train_loss= 0.69562 train_acc= 0.51429 val_loss= 0.69339 val_acc= 0.58065 time= 0.01563
Epoch: 0010 train_loss= 0.69524 train_acc= 0.51299 val_loss= 0.69324 val_acc= 0.58065 time= 0.01563
Epoch: 0011 train_loss= 0.69492 train_acc= 0.51299 val_loss= 0.69310 val_acc= 0.58065 time= 0.01563
Epoch: 0012 train_loss= 0.69484 train_acc= 0.51299 val_loss= 0.69294 val_acc= 0.58065 time= 0.01563
Epoch: 0013 train_loss= 0.69466 train_acc= 0.51429 val_loss= 0.69277 val_acc= 0.58065 time= 0.01563
Epoch: 0014 train_loss= 0.69430 train_acc= 0.51429 val_loss= 0.69261 val_acc= 0.58065 time= 0.01563
Epoch: 0015 train_loss= 0.69405 train_acc= 0.51299 val_loss= 0.69247 val_acc= 0.58065 time= 0.01563
Epoch: 0016 train_loss= 0.69404 train_acc= 0.51299 val_loss= 0.69236 val_acc= 0.58065 time= 0.01563
Epoch: 0017 train_loss= 0.69388 train_acc= 0.51299 val_loss= 0.69222 val_acc= 0.58065 time= 0.01562
Epoch: 0018 train_loss= 0.69369 train_acc= 0.51299 val_loss= 0.69207 val_acc= 0.58065 time= 0.01563
Epoch: 0019 train_loss= 0.69378 train_acc= 0.51299 val_loss= 0.69193 val_acc= 0.58065 time= 0.01563
Epoch: 0020 train_loss= 0.69341 train_acc= 0.51299 val_loss= 0.69174 val_acc= 0.58065 time= 0.01563
Epoch: 0021 train_loss= 0.69333 train_acc= 0.51299 val_loss= 0.69151 val_acc= 0.58065 time= 0.01563
Epoch: 0022 train_loss= 0.69321 train_acc= 0.51429 val_loss= 0.69128 val_acc= 0.58065 time= 0.01563
Epoch: 0023 train_loss= 0.69326 train_acc= 0.51299 val_loss= 0.69112 val_acc= 0.58065 time= 0.01563
Epoch: 0024 train_loss= 0.69313 train_acc= 0.51299 val_loss= 0.69100 val_acc= 0.58065 time= 0.01563
Epoch: 0025 train_loss= 0.69344 train_acc= 0.51299 val_loss= 0.69100 val_acc= 0.58065 time= 0.01563
Epoch: 0026 train_loss= 0.69315 train_acc= 0.51299 val_loss= 0.69108 val_acc= 0.58065 time= 0.01563
Epoch: 0027 train_loss= 0.69317 train_acc= 0.51299 val_loss= 0.69121 val_acc= 0.58065 time= 0.01563
Epoch: 0028 train_loss= 0.69309 train_acc= 0.51299 val_loss= 0.69135 val_acc= 0.58065 time= 0.01563
Epoch: 0029 train_loss= 0.69307 train_acc= 0.51299 val_loss= 0.69150 val_acc= 0.58065 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69174 accuracy= 0.55645 time= 0.00000 
