Epoch: 0001 train_loss= 1.41313 train_acc= 0.20391 val_loss= 1.39561 val_acc= 0.23214 time= 0.28127
Epoch: 0002 train_loss= 1.40628 train_acc= 0.20251 val_loss= 1.39589 val_acc= 0.26786 time= 0.01563
Epoch: 0003 train_loss= 1.42144 train_acc= 0.23324 val_loss= 1.39666 val_acc= 0.23214 time= 0.01563
Epoch: 0004 train_loss= 1.40988 train_acc= 0.24441 val_loss= 1.39792 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.39042 train_acc= 0.24022 val_loss= 1.39960 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.38759 train_acc= 0.28212 val_loss= 1.40142 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.39036 train_acc= 0.29888 val_loss= 1.40344 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.38640 train_acc= 0.29330 val_loss= 1.40576 val_acc= 0.21429 time= 0.03125
Epoch: 0009 train_loss= 1.38633 train_acc= 0.28771 val_loss= 1.40771 val_acc= 0.21429 time= 0.01563
Epoch: 0010 train_loss= 1.38610 train_acc= 0.30028 val_loss= 1.40935 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.38872 train_acc= 0.30447 val_loss= 1.41007 val_acc= 0.21429 time= 0.01562
Epoch: 0012 train_loss= 1.39245 train_acc= 0.28212 val_loss= 1.41028 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38017 accuracy= 0.29204 time= 0.00000 
