Epoch: 0001 train_loss= 1.39316 train_acc= 0.18555 val_loss= 1.39469 val_acc= 0.23214 time= 0.20315
Epoch: 0002 train_loss= 1.39043 train_acc= 0.33008 val_loss= 1.39384 val_acc= 0.23214 time= 0.01562
Epoch: 0003 train_loss= 1.38738 train_acc= 0.33008 val_loss= 1.39332 val_acc= 0.23214 time= 0.01563
Epoch: 0004 train_loss= 1.38519 train_acc= 0.33008 val_loss= 1.39358 val_acc= 0.23214 time= 0.01563
Epoch: 0005 train_loss= 1.38257 train_acc= 0.33008 val_loss= 1.39420 val_acc= 0.23214 time= 0.03125
Epoch: 0006 train_loss= 1.38076 train_acc= 0.33008 val_loss= 1.39488 val_acc= 0.23214 time= 0.01563
Epoch: 0007 train_loss= 1.37886 train_acc= 0.33008 val_loss= 1.39569 val_acc= 0.23214 time= 0.01562
Epoch: 0008 train_loss= 1.37724 train_acc= 0.33008 val_loss= 1.39663 val_acc= 0.23214 time= 0.01563
Epoch: 0009 train_loss= 1.37596 train_acc= 0.33008 val_loss= 1.39766 val_acc= 0.23214 time= 0.01563
Epoch: 0010 train_loss= 1.37471 train_acc= 0.33008 val_loss= 1.39876 val_acc= 0.23214 time= 0.01563
Epoch: 0011 train_loss= 1.37336 train_acc= 0.33008 val_loss= 1.39989 val_acc= 0.23214 time= 0.01563
Epoch: 0012 train_loss= 1.37251 train_acc= 0.33008 val_loss= 1.40110 val_acc= 0.23214 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38178 accuracy= 0.30088 time= 0.00000 
