Epoch: 0001 train_loss= 2.09462 train_acc= 0.11321 val_loss= 2.08815 val_acc= 0.13793 time= 0.26568
Epoch: 0002 train_loss= 2.09198 train_acc= 0.10063 val_loss= 2.08661 val_acc= 0.13793 time= 0.01562
Epoch: 0003 train_loss= 2.08852 train_acc= 0.13208 val_loss= 2.08514 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08533 train_acc= 0.15723 val_loss= 2.08369 val_acc= 0.20690 time= 0.00000
Epoch: 0005 train_loss= 2.08163 train_acc= 0.14465 val_loss= 2.08222 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.08126 train_acc= 0.16352 val_loss= 2.08071 val_acc= 0.17241 time= 0.01562
Epoch: 0007 train_loss= 2.07544 train_acc= 0.16352 val_loss= 2.07919 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07411 train_acc= 0.18239 val_loss= 2.07766 val_acc= 0.13793 time= 0.00000
Epoch: 0009 train_loss= 2.07136 train_acc= 0.13208 val_loss= 2.07613 val_acc= 0.13793 time= 0.01563
Epoch: 0010 train_loss= 2.06975 train_acc= 0.13836 val_loss= 2.07461 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.06583 train_acc= 0.15094 val_loss= 2.07316 val_acc= 0.13793 time= 0.00000
Epoch: 0012 train_loss= 2.06375 train_acc= 0.13208 val_loss= 2.07183 val_acc= 0.13793 time= 0.01563
Epoch: 0013 train_loss= 2.05909 train_acc= 0.15094 val_loss= 2.07059 val_acc= 0.17241 time= 0.00000
Epoch: 0014 train_loss= 2.05972 train_acc= 0.13836 val_loss= 2.06953 val_acc= 0.20690 time= 0.00000
Epoch: 0015 train_loss= 2.05818 train_acc= 0.13836 val_loss= 2.06859 val_acc= 0.20690 time= 0.01563
Epoch: 0016 train_loss= 2.05387 train_acc= 0.14465 val_loss= 2.06786 val_acc= 0.20690 time= 0.00000
Epoch: 0017 train_loss= 2.05024 train_acc= 0.16352 val_loss= 2.06725 val_acc= 0.20690 time= 0.00000
Epoch: 0018 train_loss= 2.04926 train_acc= 0.16981 val_loss= 2.06678 val_acc= 0.24138 time= 0.00000
Epoch: 0019 train_loss= 2.04766 train_acc= 0.15094 val_loss= 2.06648 val_acc= 0.20690 time= 0.01563
Epoch: 0020 train_loss= 2.04554 train_acc= 0.15094 val_loss= 2.06634 val_acc= 0.20690 time= 0.00000
Epoch: 0021 train_loss= 2.04110 train_acc= 0.15723 val_loss= 2.06626 val_acc= 0.20690 time= 0.00000
Epoch: 0022 train_loss= 2.04639 train_acc= 0.14465 val_loss= 2.06622 val_acc= 0.20690 time= 0.01563
Epoch: 0023 train_loss= 2.03800 train_acc= 0.15723 val_loss= 2.06618 val_acc= 0.20690 time= 0.00000
Epoch: 0024 train_loss= 2.03765 train_acc= 0.13836 val_loss= 2.06618 val_acc= 0.20690 time= 0.00000
Epoch: 0025 train_loss= 2.03795 train_acc= 0.14465 val_loss= 2.06605 val_acc= 0.20690 time= 0.01563
Epoch: 0026 train_loss= 2.03729 train_acc= 0.14465 val_loss= 2.06604 val_acc= 0.20690 time= 0.00000
Epoch: 0027 train_loss= 2.03787 train_acc= 0.15094 val_loss= 2.06598 val_acc= 0.20690 time= 0.00000
Epoch: 0028 train_loss= 2.03688 train_acc= 0.16981 val_loss= 2.06574 val_acc= 0.27586 time= 0.01563
Epoch: 0029 train_loss= 2.03686 train_acc= 0.18868 val_loss= 2.06553 val_acc= 0.13793 time= 0.00000
Epoch: 0030 train_loss= 2.03666 train_acc= 0.15094 val_loss= 2.06525 val_acc= 0.13793 time= 0.00000
Epoch: 0031 train_loss= 2.03265 train_acc= 0.15094 val_loss= 2.06511 val_acc= 0.13793 time= 0.01563
Epoch: 0032 train_loss= 2.04007 train_acc= 0.17610 val_loss= 2.06487 val_acc= 0.13793 time= 0.00000
Epoch: 0033 train_loss= 2.03305 train_acc= 0.18239 val_loss= 2.06467 val_acc= 0.13793 time= 0.00000
Epoch: 0034 train_loss= 2.03710 train_acc= 0.17610 val_loss= 2.06445 val_acc= 0.13793 time= 0.01563
Epoch: 0035 train_loss= 2.04058 train_acc= 0.16981 val_loss= 2.06419 val_acc= 0.13793 time= 0.00000
Epoch: 0036 train_loss= 2.03560 train_acc= 0.17610 val_loss= 2.06390 val_acc= 0.13793 time= 0.00000
Epoch: 0037 train_loss= 2.03556 train_acc= 0.17610 val_loss= 2.06377 val_acc= 0.13793 time= 0.01563
Epoch: 0038 train_loss= 2.03433 train_acc= 0.17610 val_loss= 2.06374 val_acc= 0.13793 time= 0.00000
Epoch: 0039 train_loss= 2.03942 train_acc= 0.17610 val_loss= 2.06386 val_acc= 0.13793 time= 0.00000
Epoch: 0040 train_loss= 2.03426 train_acc= 0.16981 val_loss= 2.06405 val_acc= 0.13793 time= 0.01563
Epoch: 0041 train_loss= 2.03680 train_acc= 0.17610 val_loss= 2.06433 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.01495 accuracy= 0.13559 time= 0.00000 
