Epoch: 0001 train_loss= 2.08356 train_acc= 0.11321 val_loss= 2.08468 val_acc= 0.03448 time= 0.65629
Epoch: 0002 train_loss= 2.08122 train_acc= 0.11051 val_loss= 2.08310 val_acc= 0.03448 time= 0.00000
Epoch: 0003 train_loss= 2.07967 train_acc= 0.10512 val_loss= 2.08149 val_acc= 0.20690 time= 0.01562
Epoch: 0004 train_loss= 2.07754 train_acc= 0.14555 val_loss= 2.07969 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.07542 train_acc= 0.17790 val_loss= 2.07751 val_acc= 0.20690 time= 0.01563
Epoch: 0006 train_loss= 2.07321 train_acc= 0.16981 val_loss= 2.07508 val_acc= 0.20690 time= 0.00000
Epoch: 0007 train_loss= 2.07082 train_acc= 0.17790 val_loss= 2.07242 val_acc= 0.20690 time= 0.01563
Epoch: 0008 train_loss= 2.07009 train_acc= 0.16442 val_loss= 2.06962 val_acc= 0.20690 time= 0.00000
Epoch: 0009 train_loss= 2.06718 train_acc= 0.15903 val_loss= 2.06673 val_acc= 0.20690 time= 0.01563
Epoch: 0010 train_loss= 2.06591 train_acc= 0.16712 val_loss= 2.06356 val_acc= 0.20690 time= 0.00000
Epoch: 0011 train_loss= 2.06281 train_acc= 0.17520 val_loss= 2.06006 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 2.06254 train_acc= 0.16712 val_loss= 2.05652 val_acc= 0.20690 time= 0.00000
Epoch: 0013 train_loss= 2.05919 train_acc= 0.17790 val_loss= 2.05307 val_acc= 0.20690 time= 0.01563
Epoch: 0014 train_loss= 2.05817 train_acc= 0.16981 val_loss= 2.04958 val_acc= 0.20690 time= 0.00000
Epoch: 0015 train_loss= 2.05834 train_acc= 0.17520 val_loss= 2.04637 val_acc= 0.20690 time= 0.01563
Epoch: 0016 train_loss= 2.05715 train_acc= 0.16981 val_loss= 2.04338 val_acc= 0.20690 time= 0.00000
Epoch: 0017 train_loss= 2.05802 train_acc= 0.16981 val_loss= 2.04070 val_acc= 0.20690 time= 0.01563
Epoch: 0018 train_loss= 2.05784 train_acc= 0.16981 val_loss= 2.03835 val_acc= 0.20690 time= 0.00000
Epoch: 0019 train_loss= 2.05602 train_acc= 0.17251 val_loss= 2.03651 val_acc= 0.20690 time= 0.01563
Epoch: 0020 train_loss= 2.05673 train_acc= 0.17251 val_loss= 2.03524 val_acc= 0.20690 time= 0.01562
Epoch: 0021 train_loss= 2.05509 train_acc= 0.17251 val_loss= 2.03443 val_acc= 0.20690 time= 0.00000
Epoch: 0022 train_loss= 2.05357 train_acc= 0.17251 val_loss= 2.03396 val_acc= 0.20690 time= 0.01563
Epoch: 0023 train_loss= 2.05429 train_acc= 0.16981 val_loss= 2.03363 val_acc= 0.20690 time= 0.00000
Epoch: 0024 train_loss= 2.05408 train_acc= 0.17251 val_loss= 2.03367 val_acc= 0.20690 time= 0.01563
Epoch: 0025 train_loss= 2.05357 train_acc= 0.17251 val_loss= 2.03410 val_acc= 0.20690 time= 0.00000
Epoch: 0026 train_loss= 2.05429 train_acc= 0.17251 val_loss= 2.03469 val_acc= 0.20690 time= 0.01563
Epoch: 0027 train_loss= 2.05195 train_acc= 0.17251 val_loss= 2.03557 val_acc= 0.20690 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.07475 accuracy= 0.13559 time= 0.00000 
