Epoch: 0001 train_loss= 0.69934 train_acc= 0.47818 val_loss= 0.69945 val_acc= 0.40984 time= 0.20014
Epoch: 0002 train_loss= 0.69862 train_acc= 0.53273 val_loss= 0.69987 val_acc= 0.40984 time= 0.01562
Epoch: 0003 train_loss= 0.69800 train_acc= 0.53273 val_loss= 0.70005 val_acc= 0.40984 time= 0.01563
Epoch: 0004 train_loss= 0.69744 train_acc= 0.53273 val_loss= 0.70036 val_acc= 0.40984 time= 0.01563
Epoch: 0005 train_loss= 0.69687 train_acc= 0.53273 val_loss= 0.70094 val_acc= 0.40984 time= 0.01563
Epoch: 0006 train_loss= 0.69639 train_acc= 0.53273 val_loss= 0.70187 val_acc= 0.40984 time= 0.01563
Epoch: 0007 train_loss= 0.69594 train_acc= 0.53273 val_loss= 0.70309 val_acc= 0.40984 time= 0.01563
Epoch: 0008 train_loss= 0.69556 train_acc= 0.53273 val_loss= 0.70446 val_acc= 0.40984 time= 0.01563
Epoch: 0009 train_loss= 0.69487 train_acc= 0.53273 val_loss= 0.70595 val_acc= 0.40984 time= 0.01563
Epoch: 0010 train_loss= 0.69457 train_acc= 0.53273 val_loss= 0.70738 val_acc= 0.40984 time= 0.00000
Epoch: 0011 train_loss= 0.69419 train_acc= 0.53273 val_loss= 0.70862 val_acc= 0.40984 time= 0.01563
Epoch: 0012 train_loss= 0.69424 train_acc= 0.53273 val_loss= 0.70926 val_acc= 0.40984 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.70365 accuracy= 0.44262 time= 0.01563 
