Epoch: 0001 train_loss= 2.14553 train_acc= 0.07547 val_loss= 2.15869 val_acc= 0.10345 time= 0.32815
Epoch: 0002 train_loss= 2.08364 train_acc= 0.13208 val_loss= 2.13878 val_acc= 0.13793 time= 0.01563
Epoch: 0003 train_loss= 2.10178 train_acc= 0.11950 val_loss= 2.11933 val_acc= 0.10345 time= 0.01563
Epoch: 0004 train_loss= 2.06066 train_acc= 0.20126 val_loss= 2.09942 val_acc= 0.13793 time= 0.01563
Epoch: 0005 train_loss= 2.05758 train_acc= 0.16352 val_loss= 2.08348 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.04219 train_acc= 0.18239 val_loss= 2.06534 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.02128 train_acc= 0.16981 val_loss= 2.04916 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.03337 train_acc= 0.17610 val_loss= 2.03224 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.03411 train_acc= 0.19497 val_loss= 2.01926 val_acc= 0.20690 time= 0.01563
Epoch: 0010 train_loss= 2.01136 train_acc= 0.18868 val_loss= 2.00902 val_acc= 0.20690 time= 0.01563
Epoch: 0011 train_loss= 2.01733 train_acc= 0.20126 val_loss= 1.99448 val_acc= 0.20690 time= 0.01563
Epoch: 0012 train_loss= 1.99484 train_acc= 0.24528 val_loss= 1.98109 val_acc= 0.17241 time= 0.01563
Epoch: 0013 train_loss= 2.00240 train_acc= 0.21384 val_loss= 1.96805 val_acc= 0.17241 time= 0.02078
Epoch: 0014 train_loss= 2.00410 train_acc= 0.19497 val_loss= 1.95446 val_acc= 0.17241 time= 0.01050
Epoch: 0015 train_loss= 1.99102 train_acc= 0.19497 val_loss= 1.94168 val_acc= 0.17241 time= 0.01563
Epoch: 0016 train_loss= 1.99017 train_acc= 0.18868 val_loss= 1.93374 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 1.98610 train_acc= 0.19497 val_loss= 1.92787 val_acc= 0.20690 time= 0.01563
Epoch: 0018 train_loss= 1.97623 train_acc= 0.24528 val_loss= 1.92315 val_acc= 0.20690 time= 0.01563
Epoch: 0019 train_loss= 1.99494 train_acc= 0.22013 val_loss= 1.92091 val_acc= 0.17241 time= 0.01563
Epoch: 0020 train_loss= 1.99879 train_acc= 0.21384 val_loss= 1.92008 val_acc= 0.17241 time= 0.00000
Epoch: 0021 train_loss= 2.00108 train_acc= 0.19497 val_loss= 1.92383 val_acc= 0.17241 time= 0.01563
Epoch: 0022 train_loss= 1.99107 train_acc= 0.18239 val_loss= 1.92965 val_acc= 0.17241 time= 0.01562
Epoch: 0023 train_loss= 1.99274 train_acc= 0.21384 val_loss= 1.93757 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.17620 accuracy= 0.15254 time= 0.00000 
