Epoch: 0001 train_loss= 0.70102 train_acc= 0.50000 val_loss= 0.69865 val_acc= 0.47541 time= 0.17404
Epoch: 0002 train_loss= 0.69783 train_acc= 0.51515 val_loss= 0.69700 val_acc= 0.47541 time= 0.00800
Epoch: 0003 train_loss= 0.69534 train_acc= 0.53333 val_loss= 0.69604 val_acc= 0.47541 time= 0.00800
Epoch: 0004 train_loss= 0.69365 train_acc= 0.52121 val_loss= 0.69566 val_acc= 0.47541 time= 0.00800
Epoch: 0005 train_loss= 0.69252 train_acc= 0.52424 val_loss= 0.69559 val_acc= 0.49180 time= 0.00800
Epoch: 0006 train_loss= 0.69161 train_acc= 0.52727 val_loss= 0.69573 val_acc= 0.49180 time= 0.01100
Epoch: 0007 train_loss= 0.69057 train_acc= 0.53333 val_loss= 0.69601 val_acc= 0.49180 time= 0.00700
Epoch: 0008 train_loss= 0.69045 train_acc= 0.54848 val_loss= 0.69640 val_acc= 0.49180 time= 0.00800
Epoch: 0009 train_loss= 0.68937 train_acc= 0.55152 val_loss= 0.69689 val_acc= 0.50820 time= 0.00700
Epoch: 0010 train_loss= 0.68905 train_acc= 0.54242 val_loss= 0.69740 val_acc= 0.50820 time= 0.00800
Epoch: 0011 train_loss= 0.68825 train_acc= 0.55455 val_loss= 0.69782 val_acc= 0.50820 time= 0.00800
Epoch: 0012 train_loss= 0.68790 train_acc= 0.57576 val_loss= 0.69817 val_acc= 0.50820 time= 0.00800
Early stopping...
Optimization Finished!
Test set results: cost= 0.69146 accuracy= 0.53279 time= 0.00400 
