Epoch: 0001 train_loss= 2.08707 train_acc= 0.08176 val_loss= 2.08424 val_acc= 0.10345 time= 0.09377
Epoch: 0002 train_loss= 2.08542 train_acc= 0.08805 val_loss= 2.08302 val_acc= 0.24138 time= 0.01562
Epoch: 0003 train_loss= 2.08423 train_acc= 0.16981 val_loss= 2.08199 val_acc= 0.24138 time= 0.00000
Epoch: 0004 train_loss= 2.08355 train_acc= 0.16981 val_loss= 2.08095 val_acc= 0.24138 time= 0.01562
Epoch: 0005 train_loss= 2.08302 train_acc= 0.16981 val_loss= 2.07990 val_acc= 0.24138 time= 0.01563
Epoch: 0006 train_loss= 2.08225 train_acc= 0.16981 val_loss= 2.07884 val_acc= 0.24138 time= 0.00000
Epoch: 0007 train_loss= 2.08144 train_acc= 0.16981 val_loss= 2.07775 val_acc= 0.24138 time= 0.01563
Epoch: 0008 train_loss= 2.08098 train_acc= 0.16981 val_loss= 2.07665 val_acc= 0.24138 time= 0.01563
Epoch: 0009 train_loss= 2.08020 train_acc= 0.16981 val_loss= 2.07557 val_acc= 0.24138 time= 0.00000
Epoch: 0010 train_loss= 2.07927 train_acc= 0.16981 val_loss= 2.07466 val_acc= 0.24138 time= 0.01563
Epoch: 0011 train_loss= 2.07832 train_acc= 0.16981 val_loss= 2.07381 val_acc= 0.24138 time= 0.00000
Epoch: 0012 train_loss= 2.07794 train_acc= 0.16981 val_loss= 2.07300 val_acc= 0.24138 time= 0.01563
Epoch: 0013 train_loss= 2.07695 train_acc= 0.16981 val_loss= 2.07221 val_acc= 0.24138 time= 0.01563
Epoch: 0014 train_loss= 2.07636 train_acc= 0.16981 val_loss= 2.07142 val_acc= 0.24138 time= 0.00000
Epoch: 0015 train_loss= 2.07474 train_acc= 0.16981 val_loss= 2.07062 val_acc= 0.24138 time= 0.01563
Epoch: 0016 train_loss= 2.07418 train_acc= 0.16981 val_loss= 2.06979 val_acc= 0.24138 time= 0.00000
Epoch: 0017 train_loss= 2.07428 train_acc= 0.16981 val_loss= 2.06893 val_acc= 0.24138 time= 0.01563
Epoch: 0018 train_loss= 2.07266 train_acc= 0.16981 val_loss= 2.06805 val_acc= 0.24138 time= 0.01563
Epoch: 0019 train_loss= 2.07165 train_acc= 0.16981 val_loss= 2.06714 val_acc= 0.24138 time= 0.00000
Epoch: 0020 train_loss= 2.07071 train_acc= 0.16981 val_loss= 2.06618 val_acc= 0.24138 time= 0.01563
Epoch: 0021 train_loss= 2.06955 train_acc= 0.16981 val_loss= 2.06520 val_acc= 0.24138 time= 0.01563
Epoch: 0022 train_loss= 2.06793 train_acc= 0.16981 val_loss= 2.06417 val_acc= 0.24138 time= 0.00000
Epoch: 0023 train_loss= 2.06634 train_acc= 0.16981 val_loss= 2.06312 val_acc= 0.24138 time= 0.01562
Epoch: 0024 train_loss= 2.06605 train_acc= 0.16981 val_loss= 2.06204 val_acc= 0.24138 time= 0.00000
Epoch: 0025 train_loss= 2.06377 train_acc= 0.16981 val_loss= 2.06095 val_acc= 0.24138 time= 0.01563
Epoch: 0026 train_loss= 2.06252 train_acc= 0.16981 val_loss= 2.05967 val_acc= 0.24138 time= 0.01563
Epoch: 0027 train_loss= 2.06212 train_acc= 0.16981 val_loss= 2.05819 val_acc= 0.24138 time= 0.00000
Epoch: 0028 train_loss= 2.05931 train_acc= 0.16981 val_loss= 2.05641 val_acc= 0.24138 time= 0.01563
Epoch: 0029 train_loss= 2.05900 train_acc= 0.16981 val_loss= 2.05438 val_acc= 0.24138 time= 0.01563
Epoch: 0030 train_loss= 2.05710 train_acc= 0.16981 val_loss= 2.05218 val_acc= 0.24138 time= 0.00000
Epoch: 0031 train_loss= 2.05495 train_acc= 0.16981 val_loss= 2.04987 val_acc= 0.24138 time= 0.01563
Epoch: 0032 train_loss= 2.05383 train_acc= 0.16981 val_loss= 2.04736 val_acc= 0.24138 time= 0.01562
Epoch: 0033 train_loss= 2.05373 train_acc= 0.16981 val_loss= 2.04455 val_acc= 0.24138 time= 0.00000
Epoch: 0034 train_loss= 2.05156 train_acc= 0.16981 val_loss= 2.04155 val_acc= 0.24138 time= 0.01563
Epoch: 0035 train_loss= 2.04789 train_acc= 0.16981 val_loss= 2.03823 val_acc= 0.24138 time= 0.00000
Epoch: 0036 train_loss= 2.04633 train_acc= 0.16981 val_loss= 2.03477 val_acc= 0.24138 time= 0.01563
Epoch: 0037 train_loss= 2.04729 train_acc= 0.16981 val_loss= 2.03080 val_acc= 0.24138 time= 0.01563
Epoch: 0038 train_loss= 2.04653 train_acc= 0.16981 val_loss= 2.02661 val_acc= 0.24138 time= 0.00000
Epoch: 0039 train_loss= 2.04536 train_acc= 0.16981 val_loss= 2.02235 val_acc= 0.24138 time= 0.01563
Epoch: 0040 train_loss= 2.04305 train_acc= 0.16981 val_loss= 2.01815 val_acc= 0.24138 time= 0.01563
Epoch: 0041 train_loss= 2.04515 train_acc= 0.16981 val_loss= 2.01406 val_acc= 0.24138 time= 0.00000
Epoch: 0042 train_loss= 2.04335 train_acc= 0.16981 val_loss= 2.01029 val_acc= 0.24138 time= 0.01563
Epoch: 0043 train_loss= 2.04804 train_acc= 0.16981 val_loss= 2.00667 val_acc= 0.24138 time= 0.00000
Epoch: 0044 train_loss= 2.04342 train_acc= 0.16981 val_loss= 2.00345 val_acc= 0.24138 time= 0.01562
Epoch: 0045 train_loss= 2.04374 train_acc= 0.16981 val_loss= 2.00094 val_acc= 0.24138 time= 0.01563
Epoch: 0046 train_loss= 2.04238 train_acc= 0.16981 val_loss= 1.99919 val_acc= 0.24138 time= 0.00000
Epoch: 0047 train_loss= 2.04151 train_acc= 0.15094 val_loss= 1.99804 val_acc= 0.24138 time= 0.01563
Epoch: 0048 train_loss= 2.04358 train_acc= 0.15723 val_loss= 1.99753 val_acc= 0.24138 time= 0.01562
Epoch: 0049 train_loss= 2.04291 train_acc= 0.17610 val_loss= 1.99762 val_acc= 0.24138 time= 0.00000
Epoch: 0050 train_loss= 2.04409 train_acc= 0.16981 val_loss= 1.99798 val_acc= 0.24138 time= 0.01563
Epoch: 0051 train_loss= 2.04315 train_acc= 0.15723 val_loss= 1.99864 val_acc= 0.24138 time= 0.00000
Epoch: 0052 train_loss= 2.04443 train_acc= 0.17610 val_loss= 1.99949 val_acc= 0.24138 time= 0.01563
Epoch: 0053 train_loss= 2.04163 train_acc= 0.15723 val_loss= 2.00066 val_acc= 0.24138 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.09019 accuracy= 0.06780 time= 0.00000 
