Epoch: 0001 train_loss= 2.07991 train_acc= 0.13836 val_loss= 2.06915 val_acc= 0.10345 time= 0.18751
Epoch: 0002 train_loss= 2.07546 train_acc= 0.13208 val_loss= 2.06619 val_acc= 0.06897 time= 0.01563
Epoch: 0003 train_loss= 2.07326 train_acc= 0.13836 val_loss= 2.06335 val_acc= 0.20690 time= 0.00000
Epoch: 0004 train_loss= 2.07089 train_acc= 0.16352 val_loss= 2.06049 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.06825 train_acc= 0.16352 val_loss= 2.05759 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.06457 train_acc= 0.16981 val_loss= 2.05460 val_acc= 0.20690 time= 0.00000
Epoch: 0007 train_loss= 2.06268 train_acc= 0.16352 val_loss= 2.05152 val_acc= 0.20690 time= 0.00000
Epoch: 0008 train_loss= 2.06194 train_acc= 0.16352 val_loss= 2.04833 val_acc= 0.20690 time= 0.01563
Epoch: 0009 train_loss= 2.05744 train_acc= 0.16352 val_loss= 2.04509 val_acc= 0.20690 time= 0.00000
Epoch: 0010 train_loss= 2.05421 train_acc= 0.16352 val_loss= 2.04186 val_acc= 0.20690 time= 0.00000
Epoch: 0011 train_loss= 2.05308 train_acc= 0.16352 val_loss= 2.03855 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.05309 train_acc= 0.16352 val_loss= 2.03519 val_acc= 0.20690 time= 0.00000
Epoch: 0013 train_loss= 2.05026 train_acc= 0.16352 val_loss= 2.03176 val_acc= 0.20690 time= 0.00000
Epoch: 0014 train_loss= 2.04873 train_acc= 0.16352 val_loss= 2.02823 val_acc= 0.20690 time= 0.01563
Epoch: 0015 train_loss= 2.04452 train_acc= 0.16352 val_loss= 2.02466 val_acc= 0.20690 time= 0.00000
Epoch: 0016 train_loss= 2.04197 train_acc= 0.16352 val_loss= 2.02119 val_acc= 0.20690 time= 0.00000
Epoch: 0017 train_loss= 2.04032 train_acc= 0.16352 val_loss= 2.01789 val_acc= 0.20690 time= 0.00000
Epoch: 0018 train_loss= 2.03928 train_acc= 0.16352 val_loss= 2.01477 val_acc= 0.20690 time= 0.01563
Epoch: 0019 train_loss= 2.03940 train_acc= 0.16352 val_loss= 2.01193 val_acc= 0.20690 time= 0.00000
Epoch: 0020 train_loss= 2.03639 train_acc= 0.16352 val_loss= 2.00947 val_acc= 0.20690 time= 0.01563
Epoch: 0021 train_loss= 2.03631 train_acc= 0.16352 val_loss= 2.00769 val_acc= 0.20690 time= 0.00000
Epoch: 0022 train_loss= 2.03120 train_acc= 0.16352 val_loss= 2.00688 val_acc= 0.20690 time= 0.00000
Epoch: 0023 train_loss= 2.03041 train_acc= 0.16352 val_loss= 2.00664 val_acc= 0.20690 time= 0.01563
Epoch: 0024 train_loss= 2.03079 train_acc= 0.16352 val_loss= 2.00711 val_acc= 0.20690 time= 0.00000
Epoch: 0025 train_loss= 2.03080 train_acc= 0.16352 val_loss= 2.00805 val_acc= 0.20690 time= 0.00000
Epoch: 0026 train_loss= 2.02478 train_acc= 0.16352 val_loss= 2.00953 val_acc= 0.20690 time= 0.01563
Epoch: 0027 train_loss= 2.02703 train_acc= 0.16981 val_loss= 2.01145 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08117 accuracy= 0.23729 time= 0.00000 
