Epoch: 0001 train_loss= 2.08661 train_acc= 0.08625 val_loss= 2.08499 val_acc= 0.10345 time= 0.32814
Epoch: 0002 train_loss= 2.08539 train_acc= 0.10243 val_loss= 2.08441 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08407 train_acc= 0.12938 val_loss= 2.08335 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08322 train_acc= 0.13208 val_loss= 2.08216 val_acc= 0.10345 time= 0.01562
Epoch: 0005 train_loss= 2.08262 train_acc= 0.13208 val_loss= 2.08114 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.08178 train_acc= 0.15633 val_loss= 2.08020 val_acc= 0.20690 time= 0.01562
Epoch: 0007 train_loss= 2.08091 train_acc= 0.14825 val_loss= 2.07936 val_acc= 0.20690 time= 0.00000
Epoch: 0008 train_loss= 2.08004 train_acc= 0.15094 val_loss= 2.07860 val_acc= 0.20690 time= 0.01563
Epoch: 0009 train_loss= 2.07920 train_acc= 0.15094 val_loss= 2.07790 val_acc= 0.20690 time= 0.00000
Epoch: 0010 train_loss= 2.07832 train_acc= 0.15094 val_loss= 2.07732 val_acc= 0.20690 time= 0.01563
Epoch: 0011 train_loss= 2.07719 train_acc= 0.15094 val_loss= 2.07684 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.07627 train_acc= 0.15094 val_loss= 2.07650 val_acc= 0.20690 time= 0.00000
Epoch: 0013 train_loss= 2.07518 train_acc= 0.15094 val_loss= 2.07629 val_acc= 0.20690 time= 0.01563
Epoch: 0014 train_loss= 2.07540 train_acc= 0.15094 val_loss= 2.07629 val_acc= 0.20690 time= 0.01563
Epoch: 0015 train_loss= 2.07359 train_acc= 0.15094 val_loss= 2.07646 val_acc= 0.20690 time= 0.00000
Epoch: 0016 train_loss= 2.07363 train_acc= 0.15094 val_loss= 2.07684 val_acc= 0.20690 time= 0.01563
Epoch: 0017 train_loss= 2.07237 train_acc= 0.15094 val_loss= 2.07737 val_acc= 0.20690 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.08667 accuracy= 0.13559 time= 0.00000 
