Epoch: 0001 train_loss= 0.69946 train_acc= 0.49091 val_loss= 0.69877 val_acc= 0.49180 time= 0.09333
Epoch: 0002 train_loss= 0.69878 train_acc= 0.49091 val_loss= 0.69823 val_acc= 0.49180 time= 0.01563
Epoch: 0003 train_loss= 0.69827 train_acc= 0.49091 val_loss= 0.69773 val_acc= 0.49180 time= 0.01563
Epoch: 0004 train_loss= 0.69777 train_acc= 0.49697 val_loss= 0.69727 val_acc= 0.49180 time= 0.01563
Epoch: 0005 train_loss= 0.69730 train_acc= 0.48788 val_loss= 0.69684 val_acc= 0.49180 time= 0.01563
Epoch: 0006 train_loss= 0.69686 train_acc= 0.48788 val_loss= 0.69644 val_acc= 0.49180 time= 0.00000
Epoch: 0007 train_loss= 0.69643 train_acc= 0.49091 val_loss= 0.69608 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 0.69605 train_acc= 0.50606 val_loss= 0.69574 val_acc= 0.50820 time= 0.01563
Epoch: 0009 train_loss= 0.69572 train_acc= 0.50909 val_loss= 0.69543 val_acc= 0.50820 time= 0.01563
Epoch: 0010 train_loss= 0.69542 train_acc= 0.51212 val_loss= 0.69515 val_acc= 0.50820 time= 0.01563
Epoch: 0011 train_loss= 0.69515 train_acc= 0.52424 val_loss= 0.69490 val_acc= 0.50820 time= 0.00000
Epoch: 0012 train_loss= 0.69490 train_acc= 0.52121 val_loss= 0.69467 val_acc= 0.50820 time= 0.01563
Epoch: 0013 train_loss= 0.69465 train_acc= 0.51212 val_loss= 0.69447 val_acc= 0.50820 time= 0.01563
Epoch: 0014 train_loss= 0.69446 train_acc= 0.48485 val_loss= 0.69429 val_acc= 0.50820 time= 0.01563
Epoch: 0015 train_loss= 0.69429 train_acc= 0.50000 val_loss= 0.69413 val_acc= 0.50820 time= 0.00000
Epoch: 0016 train_loss= 0.69417 train_acc= 0.47576 val_loss= 0.69399 val_acc= 0.49180 time= 0.01563
Epoch: 0017 train_loss= 0.69401 train_acc= 0.48788 val_loss= 0.69386 val_acc= 0.49180 time= 0.01563
Epoch: 0018 train_loss= 0.69389 train_acc= 0.48788 val_loss= 0.69375 val_acc= 0.49180 time= 0.01563
Epoch: 0019 train_loss= 0.69376 train_acc= 0.48788 val_loss= 0.69366 val_acc= 0.49180 time= 0.01563
Epoch: 0020 train_loss= 0.69367 train_acc= 0.49091 val_loss= 0.69358 val_acc= 0.49180 time= 0.00000
Epoch: 0021 train_loss= 0.69361 train_acc= 0.49091 val_loss= 0.69351 val_acc= 0.49180 time= 0.01563
Epoch: 0022 train_loss= 0.69349 train_acc= 0.49394 val_loss= 0.69345 val_acc= 0.49180 time= 0.01562
Epoch: 0023 train_loss= 0.69343 train_acc= 0.49091 val_loss= 0.69340 val_acc= 0.49180 time= 0.01563
Epoch: 0024 train_loss= 0.69341 train_acc= 0.49091 val_loss= 0.69336 val_acc= 0.49180 time= 0.01563
Epoch: 0025 train_loss= 0.69335 train_acc= 0.49091 val_loss= 0.69332 val_acc= 0.49180 time= 0.00000
Epoch: 0026 train_loss= 0.69334 train_acc= 0.49091 val_loss= 0.69329 val_acc= 0.49180 time= 0.02523
Epoch: 0027 train_loss= 0.69329 train_acc= 0.49091 val_loss= 0.69327 val_acc= 0.49180 time= 0.01306
Epoch: 0028 train_loss= 0.69328 train_acc= 0.49394 val_loss= 0.69325 val_acc= 0.49180 time= 0.00000
Epoch: 0029 train_loss= 0.69325 train_acc= 0.49697 val_loss= 0.69323 val_acc= 0.49180 time= 0.01562
Epoch: 0030 train_loss= 0.69326 train_acc= 0.49091 val_loss= 0.69322 val_acc= 0.49180 time= 0.01563
Epoch: 0031 train_loss= 0.69318 train_acc= 0.50303 val_loss= 0.69321 val_acc= 0.49180 time= 0.01563
Epoch: 0032 train_loss= 0.69319 train_acc= 0.52727 val_loss= 0.69320 val_acc= 0.49180 time= 0.00000
Epoch: 0033 train_loss= 0.69324 train_acc= 0.46364 val_loss= 0.69320 val_acc= 0.49180 time= 0.01563
Epoch: 0034 train_loss= 0.69320 train_acc= 0.49394 val_loss= 0.69319 val_acc= 0.49180 time= 0.01563
Epoch: 0035 train_loss= 0.69319 train_acc= 0.49394 val_loss= 0.69319 val_acc= 0.49180 time= 0.01563
Epoch: 0036 train_loss= 0.69319 train_acc= 0.51212 val_loss= 0.69319 val_acc= 0.49180 time= 0.01563
Epoch: 0037 train_loss= 0.69314 train_acc= 0.53333 val_loss= 0.69319 val_acc= 0.49180 time= 0.00000
Epoch: 0038 train_loss= 0.69325 train_acc= 0.47576 val_loss= 0.69319 val_acc= 0.49180 time= 0.01563
Epoch: 0039 train_loss= 0.69328 train_acc= 0.45152 val_loss= 0.69319 val_acc= 0.50820 time= 0.01563
Epoch: 0040 train_loss= 0.69318 train_acc= 0.49394 val_loss= 0.69319 val_acc= 0.50820 time= 0.01563
Epoch: 0041 train_loss= 0.69316 train_acc= 0.52727 val_loss= 0.69319 val_acc= 0.50820 time= 0.01563
Epoch: 0042 train_loss= 0.69317 train_acc= 0.52121 val_loss= 0.69318 val_acc= 0.49180 time= 0.00000
Epoch: 0043 train_loss= 0.69318 train_acc= 0.51212 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0044 train_loss= 0.69318 train_acc= 0.49697 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0045 train_loss= 0.69314 train_acc= 0.48485 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0046 train_loss= 0.69314 train_acc= 0.49394 val_loss= 0.69318 val_acc= 0.49180 time= 0.00000
Epoch: 0047 train_loss= 0.69315 train_acc= 0.49394 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0048 train_loss= 0.69320 train_acc= 0.49091 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0049 train_loss= 0.69311 train_acc= 0.49091 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Epoch: 0050 train_loss= 0.69315 train_acc= 0.50303 val_loss= 0.69318 val_acc= 0.49180 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69324 accuracy= 0.48361 time= 0.00000 
