Epoch: 0001 train_loss= 0.69104 train_acc= 0.53636 val_loss= 0.69233 val_acc= 0.55738 time= 0.23849
Epoch: 0002 train_loss= 0.69427 train_acc= 0.52424 val_loss= 0.69223 val_acc= 0.55738 time= 0.00000
Epoch: 0003 train_loss= 0.68796 train_acc= 0.52727 val_loss= 0.69217 val_acc= 0.55738 time= 0.01562
Epoch: 0004 train_loss= 0.69159 train_acc= 0.53333 val_loss= 0.69212 val_acc= 0.55738 time= 0.00000
Epoch: 0005 train_loss= 0.68949 train_acc= 0.53333 val_loss= 0.69213 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.68862 train_acc= 0.53030 val_loss= 0.69216 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69170 train_acc= 0.54242 val_loss= 0.69219 val_acc= 0.55738 time= 0.00000
Epoch: 0008 train_loss= 0.69036 train_acc= 0.53636 val_loss= 0.69223 val_acc= 0.55738 time= 0.00000
Epoch: 0009 train_loss= 0.69058 train_acc= 0.53333 val_loss= 0.69227 val_acc= 0.55738 time= 0.01563
Epoch: 0010 train_loss= 0.68986 train_acc= 0.53636 val_loss= 0.69229 val_acc= 0.55738 time= 0.00000
Epoch: 0011 train_loss= 0.69315 train_acc= 0.53030 val_loss= 0.69231 val_acc= 0.55738 time= 0.00000
Epoch: 0012 train_loss= 0.69169 train_acc= 0.53030 val_loss= 0.69230 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69246 accuracy= 0.52459 time= 0.01563 
