Epoch: 0001 train_loss= 0.69852 train_acc= 0.52857 val_loss= 0.69975 val_acc= 0.50820 time= 0.35884
Epoch: 0002 train_loss= 0.69774 train_acc= 0.52857 val_loss= 0.69983 val_acc= 0.50820 time= 0.00000
Epoch: 0003 train_loss= 0.69720 train_acc= 0.52857 val_loss= 0.69988 val_acc= 0.50820 time= 0.01563
Epoch: 0004 train_loss= 0.69689 train_acc= 0.52857 val_loss= 0.69974 val_acc= 0.50820 time= 0.01563
Epoch: 0005 train_loss= 0.69648 train_acc= 0.52857 val_loss= 0.69941 val_acc= 0.50820 time= 0.01563
Epoch: 0006 train_loss= 0.69591 train_acc= 0.52857 val_loss= 0.69899 val_acc= 0.50820 time= 0.01563
Epoch: 0007 train_loss= 0.69544 train_acc= 0.52857 val_loss= 0.69853 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 0.69553 train_acc= 0.52857 val_loss= 0.69800 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.69469 train_acc= 0.52857 val_loss= 0.69750 val_acc= 0.50820 time= 0.01563
Epoch: 0010 train_loss= 0.69453 train_acc= 0.52857 val_loss= 0.69703 val_acc= 0.50820 time= 0.00000
Epoch: 0011 train_loss= 0.69435 train_acc= 0.52857 val_loss= 0.69661 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.69402 train_acc= 0.52857 val_loss= 0.69625 val_acc= 0.50820 time= 0.01562
Epoch: 0013 train_loss= 0.69389 train_acc= 0.52857 val_loss= 0.69597 val_acc= 0.50820 time= 0.01563
Epoch: 0014 train_loss= 0.69362 train_acc= 0.52857 val_loss= 0.69575 val_acc= 0.50820 time= 0.01563
Epoch: 0015 train_loss= 0.69340 train_acc= 0.52857 val_loss= 0.69560 val_acc= 0.50820 time= 0.01563
Epoch: 0016 train_loss= 0.69340 train_acc= 0.52857 val_loss= 0.69549 val_acc= 0.50820 time= 0.00000
Epoch: 0017 train_loss= 0.69316 train_acc= 0.52857 val_loss= 0.69545 val_acc= 0.50820 time= 0.01563
Epoch: 0018 train_loss= 0.69293 train_acc= 0.52857 val_loss= 0.69544 val_acc= 0.50820 time= 0.01563
Epoch: 0019 train_loss= 0.69285 train_acc= 0.52857 val_loss= 0.69546 val_acc= 0.50820 time= 0.01563
Epoch: 0020 train_loss= 0.69286 train_acc= 0.52857 val_loss= 0.69547 val_acc= 0.50820 time= 0.01563
Epoch: 0021 train_loss= 0.69276 train_acc= 0.52857 val_loss= 0.69549 val_acc= 0.50820 time= 0.01563
Epoch: 0022 train_loss= 0.69275 train_acc= 0.52857 val_loss= 0.69545 val_acc= 0.50820 time= 0.01563
Epoch: 0023 train_loss= 0.69255 train_acc= 0.52857 val_loss= 0.69539 val_acc= 0.50820 time= 0.01562
Epoch: 0024 train_loss= 0.69240 train_acc= 0.52857 val_loss= 0.69534 val_acc= 0.50820 time= 0.00000
Epoch: 0025 train_loss= 0.69249 train_acc= 0.52857 val_loss= 0.69522 val_acc= 0.50820 time= 0.01563
Epoch: 0026 train_loss= 0.69225 train_acc= 0.52857 val_loss= 0.69512 val_acc= 0.50820 time= 0.01563
Epoch: 0027 train_loss= 0.69237 train_acc= 0.52857 val_loss= 0.69503 val_acc= 0.50820 time= 0.01563
Epoch: 0028 train_loss= 0.69227 train_acc= 0.52857 val_loss= 0.69497 val_acc= 0.50820 time= 0.01563
Epoch: 0029 train_loss= 0.69222 train_acc= 0.52857 val_loss= 0.69495 val_acc= 0.50820 time= 0.01563
Epoch: 0030 train_loss= 0.69241 train_acc= 0.52857 val_loss= 0.69491 val_acc= 0.50820 time= 0.01563
Epoch: 0031 train_loss= 0.69233 train_acc= 0.52857 val_loss= 0.69488 val_acc= 0.50820 time= 0.01562
Epoch: 0032 train_loss= 0.69200 train_acc= 0.52857 val_loss= 0.69496 val_acc= 0.50820 time= 0.01563
Epoch: 0033 train_loss= 0.69229 train_acc= 0.52857 val_loss= 0.69503 val_acc= 0.50820 time= 0.01563
Epoch: 0034 train_loss= 0.69215 train_acc= 0.52857 val_loss= 0.69516 val_acc= 0.50820 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69670 accuracy= 0.46721 time= 0.01563 
