Epoch: 0001 train_loss= 0.70451 train_acc= 0.45152 val_loss= 0.69868 val_acc= 0.44262 time= 0.25610
Epoch: 0002 train_loss= 0.70079 train_acc= 0.48485 val_loss= 0.69728 val_acc= 0.55738 time= 0.00000
Epoch: 0003 train_loss= 0.69864 train_acc= 0.50606 val_loss= 0.69671 val_acc= 0.55738 time= 0.00000
Epoch: 0004 train_loss= 0.69434 train_acc= 0.53636 val_loss= 0.69688 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69300 train_acc= 0.54242 val_loss= 0.69766 val_acc= 0.55738 time= 0.00000
Epoch: 0006 train_loss= 0.68960 train_acc= 0.53636 val_loss= 0.69886 val_acc= 0.55738 time= 0.00000
Epoch: 0007 train_loss= 0.68973 train_acc= 0.54242 val_loss= 0.70038 val_acc= 0.55738 time= 0.01563
Epoch: 0008 train_loss= 0.68993 train_acc= 0.54242 val_loss= 0.70194 val_acc= 0.55738 time= 0.00000
Epoch: 0009 train_loss= 0.68941 train_acc= 0.54242 val_loss= 0.70323 val_acc= 0.55738 time= 0.00000
Epoch: 0010 train_loss= 0.68985 train_acc= 0.54242 val_loss= 0.70403 val_acc= 0.55738 time= 0.01563
Epoch: 0011 train_loss= 0.69106 train_acc= 0.53333 val_loss= 0.70432 val_acc= 0.55738 time= 0.00000
Epoch: 0012 train_loss= 0.68753 train_acc= 0.54545 val_loss= 0.70423 val_acc= 0.55738 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69694 accuracy= 0.54098 time= 0.00000 
