Epoch: 0001 train_loss= 2.06972 train_acc= 0.13836 val_loss= 1.99407 val_acc= 0.24138 time= 0.29616
Epoch: 0002 train_loss= 2.06765 train_acc= 0.15094 val_loss= 1.98520 val_acc= 0.20690 time= 0.00807
Epoch: 0003 train_loss= 2.03816 train_acc= 0.16981 val_loss= 1.98565 val_acc= 0.24138 time= 0.01562
Epoch: 0004 train_loss= 2.02261 train_acc= 0.16981 val_loss= 1.99316 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.03103 train_acc= 0.20755 val_loss= 2.00391 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.01180 train_acc= 0.18239 val_loss= 2.01600 val_acc= 0.20690 time= 0.01563
Epoch: 0007 train_loss= 2.03063 train_acc= 0.13208 val_loss= 2.02549 val_acc= 0.20690 time= 0.01563
Epoch: 0008 train_loss= 2.02876 train_acc= 0.19497 val_loss= 2.03393 val_acc= 0.20690 time= 0.01563
Epoch: 0009 train_loss= 2.00786 train_acc= 0.19497 val_loss= 2.03776 val_acc= 0.20690 time= 0.01563
Epoch: 0010 train_loss= 2.00678 train_acc= 0.18868 val_loss= 2.03664 val_acc= 0.20690 time= 0.00000
Epoch: 0011 train_loss= 2.00359 train_acc= 0.18868 val_loss= 2.03565 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.00531 train_acc= 0.20755 val_loss= 2.03096 val_acc= 0.20690 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.14839 accuracy= 0.13559 time= 0.01563 
