Epoch: 0001 train_loss= 1.40224 train_acc= 0.18567 val_loss= 1.39910 val_acc= 0.16071 time= 0.25881
Epoch: 0002 train_loss= 1.39869 train_acc= 0.18567 val_loss= 1.39703 val_acc= 0.16071 time= 0.00811
Epoch: 0003 train_loss= 1.39671 train_acc= 0.18567 val_loss= 1.39526 val_acc= 0.16071 time= 0.00900
Epoch: 0004 train_loss= 1.39450 train_acc= 0.18893 val_loss= 1.39380 val_acc= 0.16071 time= 0.00800
Epoch: 0005 train_loss= 1.39284 train_acc= 0.17590 val_loss= 1.39248 val_acc= 0.17857 time= 0.00900
Epoch: 0006 train_loss= 1.39140 train_acc= 0.20521 val_loss= 1.39128 val_acc= 0.25000 time= 0.00827
Epoch: 0007 train_loss= 1.39046 train_acc= 0.20195 val_loss= 1.39018 val_acc= 0.25000 time= 0.00696
Epoch: 0008 train_loss= 1.38893 train_acc= 0.33876 val_loss= 1.38918 val_acc= 0.25000 time= 0.00900
Epoch: 0009 train_loss= 1.38839 train_acc= 0.34528 val_loss= 1.38831 val_acc= 0.26786 time= 0.01015
Epoch: 0010 train_loss= 1.38745 train_acc= 0.34853 val_loss= 1.38755 val_acc= 0.26786 time= 0.00929
Epoch: 0011 train_loss= 1.38692 train_acc= 0.34853 val_loss= 1.38689 val_acc= 0.26786 time= 0.01000
Epoch: 0012 train_loss= 1.38566 train_acc= 0.34528 val_loss= 1.38628 val_acc= 0.26786 time= 0.01003
Epoch: 0013 train_loss= 1.38532 train_acc= 0.34853 val_loss= 1.38570 val_acc= 0.26786 time= 0.01053
Epoch: 0014 train_loss= 1.38468 train_acc= 0.34528 val_loss= 1.38515 val_acc= 0.26786 time= 0.00900
Epoch: 0015 train_loss= 1.38410 train_acc= 0.34853 val_loss= 1.38458 val_acc= 0.26786 time= 0.00774
Epoch: 0016 train_loss= 1.38256 train_acc= 0.34528 val_loss= 1.38398 val_acc= 0.26786 time= 0.00908
Epoch: 0017 train_loss= 1.38080 train_acc= 0.34528 val_loss= 1.38336 val_acc= 0.26786 time= 0.00700
Epoch: 0018 train_loss= 1.37987 train_acc= 0.34853 val_loss= 1.38273 val_acc= 0.26786 time= 0.00906
Epoch: 0019 train_loss= 1.37748 train_acc= 0.34853 val_loss= 1.38211 val_acc= 0.26786 time= 0.00778
Epoch: 0020 train_loss= 1.37569 train_acc= 0.34853 val_loss= 1.38160 val_acc= 0.26786 time= 0.01000
Epoch: 0021 train_loss= 1.37324 train_acc= 0.34853 val_loss= 1.38121 val_acc= 0.26786 time= 0.00846
Epoch: 0022 train_loss= 1.37184 train_acc= 0.34853 val_loss= 1.38109 val_acc= 0.26786 time= 0.00982
Epoch: 0023 train_loss= 1.36809 train_acc= 0.34853 val_loss= 1.38124 val_acc= 0.26786 time= 0.00908
Epoch: 0024 train_loss= 1.36734 train_acc= 0.34853 val_loss= 1.38172 val_acc= 0.26786 time= 0.00800
Epoch: 0025 train_loss= 1.36521 train_acc= 0.34853 val_loss= 1.38258 val_acc= 0.26786 time= 0.01100
Early stopping...
Optimization Finished!
Test set results: cost= 1.38087 accuracy= 0.29204 time= 0.00348 
