Epoch: 0001 train_loss= 1.42683 train_acc= 0.18994 val_loss= 1.39219 val_acc= 0.26786 time= 0.31252
Epoch: 0002 train_loss= 1.42390 train_acc= 0.20391 val_loss= 1.39338 val_acc= 0.26786 time= 0.01563
Epoch: 0003 train_loss= 1.42856 train_acc= 0.19693 val_loss= 1.39523 val_acc= 0.23214 time= 0.01563
Epoch: 0004 train_loss= 1.41056 train_acc= 0.27235 val_loss= 1.39691 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.40031 train_acc= 0.26676 val_loss= 1.39861 val_acc= 0.12500 time= 0.01563
Epoch: 0006 train_loss= 1.39606 train_acc= 0.26257 val_loss= 1.40062 val_acc= 0.17857 time= 0.01563
Epoch: 0007 train_loss= 1.39082 train_acc= 0.30168 val_loss= 1.40261 val_acc= 0.19643 time= 0.03125
Epoch: 0008 train_loss= 1.39356 train_acc= 0.28212 val_loss= 1.40485 val_acc= 0.17857 time= 0.01563
Epoch: 0009 train_loss= 1.38995 train_acc= 0.28073 val_loss= 1.40718 val_acc= 0.17857 time= 0.01563
Epoch: 0010 train_loss= 1.38613 train_acc= 0.31006 val_loss= 1.40941 val_acc= 0.16071 time= 0.01563
Epoch: 0011 train_loss= 1.38707 train_acc= 0.30168 val_loss= 1.41174 val_acc= 0.16071 time= 0.01563
Epoch: 0012 train_loss= 1.38236 train_acc= 0.30307 val_loss= 1.41366 val_acc= 0.16071 time= 0.03125
Early stopping...
Optimization Finished!
Test set results: cost= 1.38385 accuracy= 0.30088 time= 0.00000 
