Epoch: 0001 train_loss= 0.84861 train_acc= 0.51515 val_loss= 1.23667 val_acc= 0.49180 time= 0.10870
Epoch: 0002 train_loss= 1.01268 train_acc= 0.49394 val_loss= 1.07448 val_acc= 0.50820 time= 0.00000
Epoch: 0003 train_loss= 1.33549 train_acc= 0.47879 val_loss= 0.83034 val_acc= 0.49180 time= 0.01563
Epoch: 0004 train_loss= 1.68317 train_acc= 0.45455 val_loss= 0.71857 val_acc= 0.50820 time= 0.01563
Epoch: 0005 train_loss= 1.16893 train_acc= 0.47576 val_loss= 0.92799 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.96060 train_acc= 0.49697 val_loss= 1.10330 val_acc= 0.52459 time= 0.00000
Epoch: 0007 train_loss= 0.95270 train_acc= 0.49394 val_loss= 1.19657 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 1.00336 train_acc= 0.50909 val_loss= 1.20040 val_acc= 0.52459 time= 0.01563
Epoch: 0009 train_loss= 1.28674 train_acc= 0.52424 val_loss= 1.09697 val_acc= 0.52459 time= 0.00000
Epoch: 0010 train_loss= 1.06155 train_acc= 0.48182 val_loss= 1.10102 val_acc= 0.52459 time= 0.01567
Epoch: 0011 train_loss= 1.40812 train_acc= 0.49697 val_loss= 1.05132 val_acc= 0.52459 time= 0.01010
Epoch: 0012 train_loss= 0.75293 train_acc= 0.53939 val_loss= 1.04332 val_acc= 0.52459 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.84821 accuracy= 0.43443 time= 0.00000 
