Epoch: 0001 train_loss= 2.08738 train_acc= 0.08895 val_loss= 2.08533 val_acc= 0.10345 time= 0.75012
Epoch: 0002 train_loss= 2.08515 train_acc= 0.13747 val_loss= 2.08318 val_acc= 0.10345 time= 0.01563
Epoch: 0003 train_loss= 2.08332 train_acc= 0.14016 val_loss= 2.08063 val_acc= 0.10345 time= 0.00000
Epoch: 0004 train_loss= 2.08137 train_acc= 0.14016 val_loss= 2.07783 val_acc= 0.10345 time= 0.01563
Epoch: 0005 train_loss= 2.07946 train_acc= 0.14016 val_loss= 2.07474 val_acc= 0.10345 time= 0.01563
Epoch: 0006 train_loss= 2.07797 train_acc= 0.14016 val_loss= 2.07151 val_acc= 0.10345 time= 0.00000
Epoch: 0007 train_loss= 2.07637 train_acc= 0.14016 val_loss= 2.06817 val_acc= 0.10345 time= 0.01562
Epoch: 0008 train_loss= 2.07432 train_acc= 0.14016 val_loss= 2.06475 val_acc= 0.10345 time= 0.00000
Epoch: 0009 train_loss= 2.07321 train_acc= 0.14016 val_loss= 2.06128 val_acc= 0.10345 time= 0.01563
Epoch: 0010 train_loss= 2.07122 train_acc= 0.14016 val_loss= 2.05772 val_acc= 0.10345 time= 0.01563
Epoch: 0011 train_loss= 2.07060 train_acc= 0.14016 val_loss= 2.05415 val_acc= 0.10345 time= 0.01563
Epoch: 0012 train_loss= 2.06963 train_acc= 0.14016 val_loss= 2.05056 val_acc= 0.10345 time= 0.00000
Epoch: 0013 train_loss= 2.06834 train_acc= 0.14016 val_loss= 2.04707 val_acc= 0.10345 time= 0.01563
Epoch: 0014 train_loss= 2.06813 train_acc= 0.14016 val_loss= 2.04375 val_acc= 0.10345 time= 0.01562
Epoch: 0015 train_loss= 2.06611 train_acc= 0.14016 val_loss= 2.04042 val_acc= 0.10345 time= 0.00000
Epoch: 0016 train_loss= 2.06570 train_acc= 0.13747 val_loss= 2.03736 val_acc= 0.10345 time= 0.01563
Epoch: 0017 train_loss= 2.06421 train_acc= 0.13747 val_loss= 2.03441 val_acc= 0.10345 time= 0.00000
Epoch: 0018 train_loss= 2.06449 train_acc= 0.14016 val_loss= 2.03147 val_acc= 0.10345 time= 0.01563
Epoch: 0019 train_loss= 2.06399 train_acc= 0.13477 val_loss= 2.02869 val_acc= 0.10345 time= 0.00000
Epoch: 0020 train_loss= 2.06242 train_acc= 0.13477 val_loss= 2.02615 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.06085 train_acc= 0.12938 val_loss= 2.02403 val_acc= 0.10345 time= 0.00000
Epoch: 0022 train_loss= 2.06214 train_acc= 0.14555 val_loss= 2.02214 val_acc= 0.24138 time= 0.01563
Epoch: 0023 train_loss= 2.06038 train_acc= 0.17790 val_loss= 2.02046 val_acc= 0.24138 time= 0.00000
Epoch: 0024 train_loss= 2.06119 train_acc= 0.15094 val_loss= 2.01918 val_acc= 0.24138 time= 0.01563
Epoch: 0025 train_loss= 2.05834 train_acc= 0.17790 val_loss= 2.01799 val_acc= 0.24138 time= 0.01563
Epoch: 0026 train_loss= 2.05791 train_acc= 0.15094 val_loss= 2.01713 val_acc= 0.24138 time= 0.00495
Epoch: 0027 train_loss= 2.06031 train_acc= 0.14286 val_loss= 2.01653 val_acc= 0.27586 time= 0.01100
Epoch: 0028 train_loss= 2.05870 train_acc= 0.14555 val_loss= 2.01626 val_acc= 0.24138 time= 0.00000
Epoch: 0029 train_loss= 2.05671 train_acc= 0.16173 val_loss= 2.01603 val_acc= 0.24138 time= 0.01563
Epoch: 0030 train_loss= 2.05830 train_acc= 0.15903 val_loss= 2.01567 val_acc= 0.24138 time= 0.00000
Epoch: 0031 train_loss= 2.05698 train_acc= 0.15903 val_loss= 2.01556 val_acc= 0.24138 time= 0.01563
Epoch: 0032 train_loss= 2.05664 train_acc= 0.17520 val_loss= 2.01539 val_acc= 0.24138 time= 0.00000
Epoch: 0033 train_loss= 2.05731 train_acc= 0.16442 val_loss= 2.01544 val_acc= 0.24138 time= 0.01563
Epoch: 0034 train_loss= 2.05674 train_acc= 0.15633 val_loss= 2.01528 val_acc= 0.24138 time= 0.00000
Epoch: 0035 train_loss= 2.05602 train_acc= 0.15364 val_loss= 2.01515 val_acc= 0.24138 time= 0.01563
Epoch: 0036 train_loss= 2.05557 train_acc= 0.14286 val_loss= 2.01517 val_acc= 0.24138 time= 0.00000
Epoch: 0037 train_loss= 2.05494 train_acc= 0.18059 val_loss= 2.01508 val_acc= 0.24138 time= 0.01563
Epoch: 0038 train_loss= 2.05390 train_acc= 0.16173 val_loss= 2.01490 val_acc= 0.24138 time= 0.01563
Epoch: 0039 train_loss= 2.05303 train_acc= 0.17790 val_loss= 2.01454 val_acc= 0.20690 time= 0.00000
Epoch: 0040 train_loss= 2.05423 train_acc= 0.16442 val_loss= 2.01429 val_acc= 0.13793 time= 0.01563
Epoch: 0041 train_loss= 2.05304 train_acc= 0.17520 val_loss= 2.01393 val_acc= 0.10345 time= 0.00000
Epoch: 0042 train_loss= 2.05423 train_acc= 0.14825 val_loss= 2.01375 val_acc= 0.10345 time= 0.01563
Epoch: 0043 train_loss= 2.05193 train_acc= 0.16712 val_loss= 2.01361 val_acc= 0.10345 time= 0.00000
Epoch: 0044 train_loss= 2.05181 train_acc= 0.16442 val_loss= 2.01324 val_acc= 0.10345 time= 0.01563
Epoch: 0045 train_loss= 2.05265 train_acc= 0.15903 val_loss= 2.01284 val_acc= 0.10345 time= 0.00000
Epoch: 0046 train_loss= 2.05124 train_acc= 0.16981 val_loss= 2.01270 val_acc= 0.10345 time= 0.01563
Epoch: 0047 train_loss= 2.05152 train_acc= 0.16173 val_loss= 2.01272 val_acc= 0.10345 time= 0.01563
Epoch: 0048 train_loss= 2.05271 train_acc= 0.15633 val_loss= 2.01302 val_acc= 0.10345 time= 0.00000
Epoch: 0049 train_loss= 2.05150 train_acc= 0.16712 val_loss= 2.01346 val_acc= 0.10345 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07242 accuracy= 0.11864 time= 0.00000 
