Epoch: 0001 train_loss= 0.70119 train_acc= 0.50909 val_loss= 0.69811 val_acc= 0.54098 time= 0.12501
Epoch: 0002 train_loss= 0.69805 train_acc= 0.50909 val_loss= 0.69587 val_acc= 0.54098 time= 0.01563
Epoch: 0003 train_loss= 0.69569 train_acc= 0.50909 val_loss= 0.69436 val_acc= 0.54098 time= 0.01562
Epoch: 0004 train_loss= 0.69428 train_acc= 0.50909 val_loss= 0.69348 val_acc= 0.54098 time= 0.00000
Epoch: 0005 train_loss= 0.69329 train_acc= 0.50909 val_loss= 0.69305 val_acc= 0.54098 time= 0.01563
Epoch: 0006 train_loss= 0.69258 train_acc= 0.50909 val_loss= 0.69292 val_acc= 0.54098 time= 0.01563
Epoch: 0007 train_loss= 0.69259 train_acc= 0.50909 val_loss= 0.69297 val_acc= 0.54098 time= 0.00000
Epoch: 0008 train_loss= 0.69219 train_acc= 0.50909 val_loss= 0.69308 val_acc= 0.54098 time= 0.01563
Epoch: 0009 train_loss= 0.69222 train_acc= 0.50909 val_loss= 0.69320 val_acc= 0.54098 time= 0.01563
Epoch: 0010 train_loss= 0.69179 train_acc= 0.51212 val_loss= 0.69330 val_acc= 0.54098 time= 0.00000
Epoch: 0011 train_loss= 0.69153 train_acc= 0.52121 val_loss= 0.69337 val_acc= 0.54098 time= 0.01563
Epoch: 0012 train_loss= 0.69139 train_acc= 0.51212 val_loss= 0.69338 val_acc= 0.54098 time= 0.01563
Epoch: 0013 train_loss= 0.69178 train_acc= 0.52424 val_loss= 0.69334 val_acc= 0.54098 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 0.69269 accuracy= 0.52459 time= 0.00000 
