Epoch: 0001 train_loss= 2.08520 train_acc= 0.16173 val_loss= 2.08600 val_acc= 0.20690 time= 0.60814
Epoch: 0002 train_loss= 2.08398 train_acc= 0.16173 val_loss= 2.08327 val_acc= 0.20690 time= 0.00800
Epoch: 0003 train_loss= 2.08263 train_acc= 0.16981 val_loss= 2.08039 val_acc= 0.20690 time= 0.00800
Epoch: 0004 train_loss= 2.08122 train_acc= 0.16712 val_loss= 2.07758 val_acc= 0.20690 time= 0.00800
Epoch: 0005 train_loss= 2.07997 train_acc= 0.16712 val_loss= 2.07467 val_acc= 0.20690 time= 0.00800
Epoch: 0006 train_loss= 2.07880 train_acc= 0.17520 val_loss= 2.07161 val_acc= 0.20690 time= 0.01000
Epoch: 0007 train_loss= 2.07698 train_acc= 0.17251 val_loss= 2.06842 val_acc= 0.20690 time= 0.00900
Epoch: 0008 train_loss= 2.07556 train_acc= 0.16712 val_loss= 2.06519 val_acc= 0.20690 time= 0.00800
Epoch: 0009 train_loss= 2.07414 train_acc= 0.17251 val_loss= 2.06195 val_acc= 0.20690 time= 0.00900
Epoch: 0010 train_loss= 2.07257 train_acc= 0.17251 val_loss= 2.05883 val_acc= 0.20690 time= 0.00900
Epoch: 0011 train_loss= 2.07221 train_acc= 0.16981 val_loss= 2.05580 val_acc= 0.20690 time= 0.01100
Epoch: 0012 train_loss= 2.06957 train_acc= 0.16981 val_loss= 2.05281 val_acc= 0.20690 time= 0.01100
Epoch: 0013 train_loss= 2.06900 train_acc= 0.16173 val_loss= 2.05002 val_acc= 0.20690 time= 0.00800
Epoch: 0014 train_loss= 2.06911 train_acc= 0.16442 val_loss= 2.04746 val_acc= 0.20690 time= 0.01000
Epoch: 0015 train_loss= 2.06724 train_acc= 0.16712 val_loss= 2.04505 val_acc= 0.20690 time= 0.01000
Epoch: 0016 train_loss= 2.06463 train_acc= 0.17251 val_loss= 2.04265 val_acc= 0.20690 time= 0.00900
Epoch: 0017 train_loss= 2.06552 train_acc= 0.16981 val_loss= 2.04009 val_acc= 0.20690 time= 0.00913
Epoch: 0018 train_loss= 2.06362 train_acc= 0.16173 val_loss= 2.03769 val_acc= 0.20690 time= 0.00788
Epoch: 0019 train_loss= 2.06344 train_acc= 0.16981 val_loss= 2.03538 val_acc= 0.20690 time= 0.01000
Epoch: 0020 train_loss= 2.06242 train_acc= 0.16981 val_loss= 2.03286 val_acc= 0.20690 time= 0.01100
Epoch: 0021 train_loss= 2.06269 train_acc= 0.16981 val_loss= 2.03030 val_acc= 0.20690 time= 0.00800
Epoch: 0022 train_loss= 2.06235 train_acc= 0.16981 val_loss= 2.02773 val_acc= 0.20690 time= 0.00800
Epoch: 0023 train_loss= 2.06094 train_acc= 0.16981 val_loss= 2.02511 val_acc= 0.20690 time= 0.00800
Epoch: 0024 train_loss= 2.05884 train_acc= 0.16981 val_loss= 2.02257 val_acc= 0.20690 time= 0.00800
Epoch: 0025 train_loss= 2.05966 train_acc= 0.17251 val_loss= 2.02009 val_acc= 0.20690 time= 0.00800
Epoch: 0026 train_loss= 2.06067 train_acc= 0.16712 val_loss= 2.01755 val_acc= 0.20690 time= 0.00900
Epoch: 0027 train_loss= 2.05720 train_acc= 0.16442 val_loss= 2.01502 val_acc= 0.20690 time= 0.00800
Epoch: 0028 train_loss= 2.05664 train_acc= 0.16981 val_loss= 2.01269 val_acc= 0.20690 time= 0.00800
Epoch: 0029 train_loss= 2.05787 train_acc= 0.16712 val_loss= 2.01036 val_acc= 0.20690 time= 0.00800
Epoch: 0030 train_loss= 2.05640 train_acc= 0.16173 val_loss= 2.00786 val_acc= 0.20690 time= 0.00800
Epoch: 0031 train_loss= 2.05707 train_acc= 0.16981 val_loss= 2.00545 val_acc= 0.20690 time= 0.01100
Epoch: 0032 train_loss= 2.05550 train_acc= 0.16442 val_loss= 2.00294 val_acc= 0.20690 time= 0.00800
Epoch: 0033 train_loss= 2.05479 train_acc= 0.16173 val_loss= 2.00034 val_acc= 0.20690 time= 0.00800
Epoch: 0034 train_loss= 2.05572 train_acc= 0.16981 val_loss= 1.99807 val_acc= 0.24138 time= 0.01000
Epoch: 0035 train_loss= 2.05679 train_acc= 0.14825 val_loss= 1.99626 val_acc= 0.24138 time= 0.00900
Epoch: 0036 train_loss= 2.05588 train_acc= 0.16981 val_loss= 1.99476 val_acc= 0.24138 time= 0.00900
Epoch: 0037 train_loss= 2.05625 train_acc= 0.16712 val_loss= 1.99346 val_acc= 0.24138 time= 0.00900
Epoch: 0038 train_loss= 2.05469 train_acc= 0.16173 val_loss= 1.99230 val_acc= 0.24138 time= 0.00800
Epoch: 0039 train_loss= 2.05311 train_acc= 0.15903 val_loss= 1.99133 val_acc= 0.24138 time= 0.00800
Epoch: 0040 train_loss= 2.05560 train_acc= 0.16173 val_loss= 1.99064 val_acc= 0.24138 time= 0.00800
Epoch: 0041 train_loss= 2.05370 train_acc= 0.15633 val_loss= 1.99005 val_acc= 0.24138 time= 0.00800
Epoch: 0042 train_loss= 2.05561 train_acc= 0.16442 val_loss= 1.98978 val_acc= 0.24138 time= 0.00800
Epoch: 0043 train_loss= 2.05551 train_acc= 0.16442 val_loss= 1.99006 val_acc= 0.24138 time= 0.00800
Epoch: 0044 train_loss= 2.05330 train_acc= 0.16981 val_loss= 1.99025 val_acc= 0.24138 time= 0.00700
Epoch: 0045 train_loss= 2.05455 train_acc= 0.15903 val_loss= 1.99064 val_acc= 0.24138 time= 0.00900
Epoch: 0046 train_loss= 2.05606 train_acc= 0.16442 val_loss= 1.99115 val_acc= 0.24138 time= 0.00800
Epoch: 0047 train_loss= 2.05367 train_acc= 0.18868 val_loss= 1.99185 val_acc= 0.24138 time= 0.00900
Early stopping...
Optimization Finished!
Test set results: cost= 2.07527 accuracy= 0.11864 time= 0.00300 
