Epoch: 0001 train_loss= 2.07936 train_acc= 0.10243 val_loss= 2.08082 val_acc= 0.10345 time= 0.90701
Epoch: 0002 train_loss= 2.07815 train_acc= 0.10243 val_loss= 2.07751 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.07489 train_acc= 0.10512 val_loss= 2.07408 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.07355 train_acc= 0.10512 val_loss= 2.07048 val_acc= 0.10345 time= 0.00000
Epoch: 0005 train_loss= 2.07209 train_acc= 0.10512 val_loss= 2.06692 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07101 train_acc= 0.10782 val_loss= 2.06331 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.06883 train_acc= 0.10782 val_loss= 2.05959 val_acc= 0.20690 time= 0.01563
Epoch: 0008 train_loss= 2.06548 train_acc= 0.13477 val_loss= 2.05591 val_acc= 0.20690 time= 0.00000
Epoch: 0009 train_loss= 2.06233 train_acc= 0.17251 val_loss= 2.05220 val_acc= 0.20690 time= 0.00000
Epoch: 0010 train_loss= 2.06226 train_acc= 0.16981 val_loss= 2.04855 val_acc= 0.20690 time= 0.01563
Epoch: 0011 train_loss= 2.06000 train_acc= 0.17520 val_loss= 2.04496 val_acc= 0.20690 time= 0.00000
Epoch: 0012 train_loss= 2.06075 train_acc= 0.14016 val_loss= 2.04152 val_acc= 0.20690 time= 0.00000
Epoch: 0013 train_loss= 2.05716 train_acc= 0.15094 val_loss= 2.03822 val_acc= 0.20690 time= 0.01563
Epoch: 0014 train_loss= 2.05686 train_acc= 0.16712 val_loss= 2.03507 val_acc= 0.20690 time= 0.00000
Epoch: 0015 train_loss= 2.05649 train_acc= 0.16173 val_loss= 2.03215 val_acc= 0.13793 time= 0.00000
Epoch: 0016 train_loss= 2.05194 train_acc= 0.21563 val_loss= 2.02956 val_acc= 0.03448 time= 0.01562
Epoch: 0017 train_loss= 2.05166 train_acc= 0.16712 val_loss= 2.02731 val_acc= 0.06897 time= 0.00000
Epoch: 0018 train_loss= 2.05096 train_acc= 0.19137 val_loss= 2.02559 val_acc= 0.06897 time= 0.00000
Epoch: 0019 train_loss= 2.04745 train_acc= 0.19137 val_loss= 2.02420 val_acc= 0.06897 time= 0.01563
Epoch: 0020 train_loss= 2.04977 train_acc= 0.18329 val_loss= 2.02371 val_acc= 0.06897 time= 0.00000
Epoch: 0021 train_loss= 2.04902 train_acc= 0.16981 val_loss= 2.02428 val_acc= 0.06897 time= 0.00000
Epoch: 0022 train_loss= 2.04779 train_acc= 0.18329 val_loss= 2.02539 val_acc= 0.06897 time= 0.01563
Epoch: 0023 train_loss= 2.04885 train_acc= 0.18059 val_loss= 2.02738 val_acc= 0.06897 time= 0.00000
Epoch: 0024 train_loss= 2.04603 train_acc= 0.18059 val_loss= 2.03018 val_acc= 0.06897 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07302 accuracy= 0.11864 time= 0.01563 
