Epoch: 0001 train_loss= 1.22455 train_acc= 0.53091 val_loss= 0.74194 val_acc= 0.54839 time= 0.64845
Epoch: 0002 train_loss= 1.33015 train_acc= 0.51091 val_loss= 0.74925 val_acc= 0.53226 time= 0.02201
Epoch: 0003 train_loss= 0.74475 train_acc= 0.50909 val_loss= 0.73870 val_acc= 0.56452 time= 0.02301
Epoch: 0004 train_loss= 1.28185 train_acc= 0.50182 val_loss= 0.69734 val_acc= 0.53226 time= 0.02501
Epoch: 0005 train_loss= 1.83137 train_acc= 0.49636 val_loss= 0.71874 val_acc= 0.48387 time= 0.02301
Epoch: 0006 train_loss= 1.30618 train_acc= 0.48909 val_loss= 0.74503 val_acc= 0.51613 time= 0.02200
Epoch: 0007 train_loss= 1.45686 train_acc= 0.52364 val_loss= 0.71782 val_acc= 0.48387 time= 0.02301
Epoch: 0008 train_loss= 2.37694 train_acc= 0.52364 val_loss= 0.69484 val_acc= 0.53226 time= 0.02601
Epoch: 0009 train_loss= 1.59055 train_acc= 0.51455 val_loss= 0.71083 val_acc= 0.56452 time= 0.02501
Epoch: 0010 train_loss= 1.04527 train_acc= 0.51273 val_loss= 0.74939 val_acc= 0.53226 time= 0.02201
Epoch: 0011 train_loss= 1.50451 train_acc= 0.49273 val_loss= 0.77428 val_acc= 0.53226 time= 0.02200
Epoch: 0012 train_loss= 2.23032 train_acc= 0.47818 val_loss= 0.76590 val_acc= 0.53226 time= 0.02101
Early stopping...
Optimization Finished!
Test set results: cost= 0.78060 accuracy= 0.38710 time= 0.01100 
