Epoch: 0001 train_loss= 1.39380 train_acc= 0.36156 val_loss= 1.39327 val_acc= 0.21429 time= 0.16407
Epoch: 0002 train_loss= 1.38855 train_acc= 0.36156 val_loss= 1.39336 val_acc= 0.21429 time= 0.01563
Epoch: 0003 train_loss= 1.38359 train_acc= 0.36156 val_loss= 1.39449 val_acc= 0.21429 time= 0.01562
Epoch: 0004 train_loss= 1.37881 train_acc= 0.36156 val_loss= 1.39667 val_acc= 0.21429 time= 0.01563
Epoch: 0005 train_loss= 1.37484 train_acc= 0.36156 val_loss= 1.39983 val_acc= 0.21429 time= 0.01562
Epoch: 0006 train_loss= 1.37116 train_acc= 0.36156 val_loss= 1.40385 val_acc= 0.21429 time= 0.01563
Epoch: 0007 train_loss= 1.36931 train_acc= 0.36156 val_loss= 1.40849 val_acc= 0.21429 time= 0.01563
Epoch: 0008 train_loss= 1.36625 train_acc= 0.36156 val_loss= 1.41358 val_acc= 0.21429 time= 0.01563
Epoch: 0009 train_loss= 1.36656 train_acc= 0.36156 val_loss= 1.41872 val_acc= 0.21429 time= 0.01563
Epoch: 0010 train_loss= 1.36439 train_acc= 0.36156 val_loss= 1.42371 val_acc= 0.21429 time= 0.01563
Epoch: 0011 train_loss= 1.36371 train_acc= 0.36156 val_loss= 1.42823 val_acc= 0.21429 time= 0.01563
Epoch: 0012 train_loss= 1.36259 train_acc= 0.36156 val_loss= 1.43205 val_acc= 0.21429 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.40018 accuracy= 0.29204 time= 0.00000 
