Epoch: 0001 train_loss= 0.69695 train_acc= 0.46727 val_loss= 0.69325 val_acc= 0.51613 time= 0.58993
Epoch: 0002 train_loss= 0.69555 train_acc= 0.49273 val_loss= 0.69543 val_acc= 0.54839 time= 0.01562
Epoch: 0003 train_loss= 0.69238 train_acc= 0.55091 val_loss= 0.69789 val_acc= 0.45161 time= 0.00000
Epoch: 0004 train_loss= 0.69351 train_acc= 0.50727 val_loss= 0.70049 val_acc= 0.45161 time= 0.00000
Epoch: 0005 train_loss= 0.69413 train_acc= 0.53273 val_loss= 0.70307 val_acc= 0.45161 time= 0.01563
Epoch: 0006 train_loss= 0.69411 train_acc= 0.53091 val_loss= 0.70555 val_acc= 0.45161 time= 0.00000
Epoch: 0007 train_loss= 0.69186 train_acc= 0.55091 val_loss= 0.70790 val_acc= 0.45161 time= 0.01563
Epoch: 0008 train_loss= 0.69507 train_acc= 0.54182 val_loss= 0.71001 val_acc= 0.45161 time= 0.00000
Epoch: 0009 train_loss= 0.69118 train_acc= 0.55091 val_loss= 0.71133 val_acc= 0.45161 time= 0.00000
Epoch: 0010 train_loss= 0.69295 train_acc= 0.55091 val_loss= 0.71224 val_acc= 0.45161 time= 0.01563
Epoch: 0011 train_loss= 0.69070 train_acc= 0.54727 val_loss= 0.71268 val_acc= 0.45161 time= 0.00000
Epoch: 0012 train_loss= 0.69184 train_acc= 0.54364 val_loss= 0.71296 val_acc= 0.45161 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.68026 accuracy= 0.55645 time= 0.00000 
