Epoch: 0001 train_loss= 0.84621 train_acc= 0.51515 val_loss= 0.76037 val_acc= 0.45902 time= 0.29689
Epoch: 0002 train_loss= 0.81782 train_acc= 0.44242 val_loss= 0.78909 val_acc= 0.47541 time= 0.01563
Epoch: 0003 train_loss= 0.72180 train_acc= 0.51212 val_loss= 0.78584 val_acc= 0.44262 time= 0.03125
Epoch: 0004 train_loss= 0.77058 train_acc= 0.53333 val_loss= 0.76602 val_acc= 0.44262 time= 0.01563
Epoch: 0005 train_loss= 0.74130 train_acc= 0.53030 val_loss= 0.74667 val_acc= 0.45902 time= 0.01563
Epoch: 0006 train_loss= 0.77475 train_acc= 0.56061 val_loss= 0.74442 val_acc= 0.45902 time= 0.03125
Epoch: 0007 train_loss= 0.69601 train_acc= 0.52424 val_loss= 0.75160 val_acc= 0.50820 time= 0.03437
Epoch: 0008 train_loss= 0.72355 train_acc= 0.54242 val_loss= 0.76126 val_acc= 0.50820 time= 0.02401
Epoch: 0009 train_loss= 0.70619 train_acc= 0.55758 val_loss= 0.76637 val_acc= 0.50820 time= 0.02601
Epoch: 0010 train_loss= 0.73088 train_acc= 0.56364 val_loss= 0.76865 val_acc= 0.50820 time= 0.02301
Epoch: 0011 train_loss= 0.74557 train_acc= 0.54848 val_loss= 0.77160 val_acc= 0.50820 time= 0.02000
Epoch: 0012 train_loss= 0.70630 train_acc= 0.54848 val_loss= 0.77049 val_acc= 0.50820 time= 0.02201
Early stopping...
Optimization Finished!
Test set results: cost= 0.83648 accuracy= 0.54918 time= 0.01300 
