Epoch: 0001 train_loss= 2.08764 train_acc= 0.06918 val_loss= 2.08543 val_acc= 0.17241 time= 0.14063
Epoch: 0002 train_loss= 2.08530 train_acc= 0.15723 val_loss= 2.08373 val_acc= 0.17241 time= 0.01563
Epoch: 0003 train_loss= 2.08346 train_acc= 0.19497 val_loss= 2.08223 val_acc= 0.17241 time= 0.00000
Epoch: 0004 train_loss= 2.08157 train_acc= 0.17610 val_loss= 2.08096 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.08035 train_acc= 0.17610 val_loss= 2.07991 val_acc= 0.17241 time= 0.01563
Epoch: 0006 train_loss= 2.07858 train_acc= 0.16981 val_loss= 2.07907 val_acc= 0.17241 time= 0.00000
Epoch: 0007 train_loss= 2.07720 train_acc= 0.16981 val_loss= 2.07834 val_acc= 0.17241 time= 0.01563
Epoch: 0008 train_loss= 2.07596 train_acc= 0.15723 val_loss= 2.07766 val_acc= 0.17241 time= 0.00000
Epoch: 0009 train_loss= 2.07538 train_acc= 0.18239 val_loss= 2.07706 val_acc= 0.17241 time= 0.01563
Epoch: 0010 train_loss= 2.07354 train_acc= 0.17610 val_loss= 2.07638 val_acc= 0.17241 time= 0.00000
Epoch: 0011 train_loss= 2.07237 train_acc= 0.18868 val_loss= 2.07567 val_acc= 0.17241 time= 0.01563
Epoch: 0012 train_loss= 2.07187 train_acc= 0.16352 val_loss= 2.07491 val_acc= 0.17241 time= 0.00000
Epoch: 0013 train_loss= 2.07047 train_acc= 0.16352 val_loss= 2.07399 val_acc= 0.17241 time= 0.01563
Epoch: 0014 train_loss= 2.06867 train_acc= 0.16352 val_loss= 2.07301 val_acc= 0.17241 time= 0.00000
Epoch: 0015 train_loss= 2.06746 train_acc= 0.15723 val_loss= 2.07194 val_acc= 0.17241 time= 0.00000
Epoch: 0016 train_loss= 2.06633 train_acc= 0.20126 val_loss= 2.07076 val_acc= 0.17241 time= 0.01563
Epoch: 0017 train_loss= 2.06507 train_acc= 0.18239 val_loss= 2.06951 val_acc= 0.17241 time= 0.00000
Epoch: 0018 train_loss= 2.06234 train_acc= 0.17610 val_loss= 2.06826 val_acc= 0.17241 time= 0.01563
Epoch: 0019 train_loss= 2.06381 train_acc= 0.12579 val_loss= 2.06700 val_acc= 0.17241 time= 0.00000
Epoch: 0020 train_loss= 2.06106 train_acc= 0.18239 val_loss= 2.06589 val_acc= 0.17241 time= 0.01563
Epoch: 0021 train_loss= 2.06064 train_acc= 0.15723 val_loss= 2.06507 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.05896 train_acc= 0.16981 val_loss= 2.06452 val_acc= 0.17241 time= 0.01563
Epoch: 0023 train_loss= 2.05761 train_acc= 0.15723 val_loss= 2.06404 val_acc= 0.17241 time= 0.00000
Epoch: 0024 train_loss= 2.05646 train_acc= 0.18239 val_loss= 2.06378 val_acc= 0.17241 time= 0.00000
Epoch: 0025 train_loss= 2.05456 train_acc= 0.16981 val_loss= 2.06364 val_acc= 0.17241 time= 0.01563
Epoch: 0026 train_loss= 2.05592 train_acc= 0.14465 val_loss= 2.06368 val_acc= 0.17241 time= 0.00000
Epoch: 0027 train_loss= 2.05391 train_acc= 0.18868 val_loss= 2.06372 val_acc= 0.17241 time= 0.01562
Epoch: 0028 train_loss= 2.05375 train_acc= 0.17610 val_loss= 2.06370 val_acc= 0.17241 time= 0.00000
Epoch: 0029 train_loss= 2.05442 train_acc= 0.18239 val_loss= 2.06374 val_acc= 0.17241 time= 0.01563
Epoch: 0030 train_loss= 2.05400 train_acc= 0.17610 val_loss= 2.06392 val_acc= 0.17241 time= 0.00000
Epoch: 0031 train_loss= 2.05129 train_acc= 0.18239 val_loss= 2.06421 val_acc= 0.17241 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.08239 accuracy= 0.10169 time= 0.00000 
