Epoch: 0001 train_loss= 1.39634 train_acc= 0.23453 val_loss= 1.41144 val_acc= 0.15000 time= 0.12501
Epoch: 0002 train_loss= 1.40328 train_acc= 0.23779 val_loss= 1.41103 val_acc= 0.20000 time= 0.01563
Epoch: 0003 train_loss= 1.38830 train_acc= 0.25081 val_loss= 1.41170 val_acc= 0.23333 time= 0.01563
Epoch: 0004 train_loss= 1.39132 train_acc= 0.24430 val_loss= 1.41352 val_acc= 0.28333 time= 0.01563
Epoch: 0005 train_loss= 1.37561 train_acc= 0.32899 val_loss= 1.41570 val_acc= 0.26667 time= 0.01563
Epoch: 0006 train_loss= 1.36949 train_acc= 0.34853 val_loss= 1.41848 val_acc= 0.26667 time= 0.01563
Epoch: 0007 train_loss= 1.36094 train_acc= 0.36808 val_loss= 1.42235 val_acc= 0.26667 time= 0.01563
Epoch: 0008 train_loss= 1.36070 train_acc= 0.36482 val_loss= 1.42558 val_acc= 0.26667 time= 0.01563
Epoch: 0009 train_loss= 1.36596 train_acc= 0.36482 val_loss= 1.42838 val_acc= 0.26667 time= 0.01563
Epoch: 0010 train_loss= 1.36360 train_acc= 0.36482 val_loss= 1.43196 val_acc= 0.26667 time= 0.01563
Epoch: 0011 train_loss= 1.36354 train_acc= 0.36482 val_loss= 1.43620 val_acc= 0.26667 time= 0.01563
Epoch: 0012 train_loss= 1.35264 train_acc= 0.36482 val_loss= 1.44049 val_acc= 0.26667 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38504 accuracy= 0.31667 time= 0.00000 
