Epoch: 0001 train_loss= 0.69935 train_acc= 0.47879 val_loss= 0.69928 val_acc= 0.47541 time= 0.06428
Epoch: 0002 train_loss= 0.69851 train_acc= 0.49394 val_loss= 0.69882 val_acc= 0.47541 time= 0.01401
Epoch: 0003 train_loss= 0.69787 train_acc= 0.50303 val_loss= 0.69845 val_acc= 0.47541 time= 0.01563
Epoch: 0004 train_loss= 0.69766 train_acc= 0.49697 val_loss= 0.69805 val_acc= 0.47541 time= 0.00000
Epoch: 0005 train_loss= 0.69730 train_acc= 0.49394 val_loss= 0.69761 val_acc= 0.47541 time= 0.01562
Epoch: 0006 train_loss= 0.69691 train_acc= 0.49697 val_loss= 0.69718 val_acc= 0.47541 time= 0.01563
Epoch: 0007 train_loss= 0.69641 train_acc= 0.49394 val_loss= 0.69678 val_acc= 0.47541 time= 0.00000
Epoch: 0008 train_loss= 0.69611 train_acc= 0.48788 val_loss= 0.69634 val_acc= 0.47541 time= 0.01563
Epoch: 0009 train_loss= 0.69582 train_acc= 0.49091 val_loss= 0.69589 val_acc= 0.47541 time= 0.00000
Epoch: 0010 train_loss= 0.69553 train_acc= 0.48485 val_loss= 0.69551 val_acc= 0.47541 time= 0.01562
Epoch: 0011 train_loss= 0.69534 train_acc= 0.47576 val_loss= 0.69518 val_acc= 0.47541 time= 0.01563
Epoch: 0012 train_loss= 0.69491 train_acc= 0.50000 val_loss= 0.69482 val_acc= 0.47541 time= 0.00000
Epoch: 0013 train_loss= 0.69479 train_acc= 0.50303 val_loss= 0.69453 val_acc= 0.52459 time= 0.01563
Epoch: 0014 train_loss= 0.69456 train_acc= 0.48485 val_loss= 0.69428 val_acc= 0.52459 time= 0.01563
Epoch: 0015 train_loss= 0.69459 train_acc= 0.48182 val_loss= 0.69418 val_acc= 0.52459 time= 0.00000
Epoch: 0016 train_loss= 0.69420 train_acc= 0.48485 val_loss= 0.69415 val_acc= 0.47541 time= 0.01563
Epoch: 0017 train_loss= 0.69401 train_acc= 0.54545 val_loss= 0.69405 val_acc= 0.47541 time= 0.00000
Epoch: 0018 train_loss= 0.69380 train_acc= 0.52727 val_loss= 0.69399 val_acc= 0.47541 time= 0.01563
Epoch: 0019 train_loss= 0.69383 train_acc= 0.50303 val_loss= 0.69397 val_acc= 0.47541 time= 0.01563
Epoch: 0020 train_loss= 0.69371 train_acc= 0.49091 val_loss= 0.69397 val_acc= 0.47541 time= 0.00000
Epoch: 0021 train_loss= 0.69375 train_acc= 0.48182 val_loss= 0.69397 val_acc= 0.47541 time= 0.01562
Epoch: 0022 train_loss= 0.69351 train_acc= 0.49394 val_loss= 0.69398 val_acc= 0.47541 time= 0.01563
Epoch: 0023 train_loss= 0.69357 train_acc= 0.48182 val_loss= 0.69401 val_acc= 0.47541 time= 0.00000
Epoch: 0024 train_loss= 0.69359 train_acc= 0.48485 val_loss= 0.69399 val_acc= 0.47541 time= 0.01563
Epoch: 0025 train_loss= 0.69343 train_acc= 0.49394 val_loss= 0.69395 val_acc= 0.47541 time= 0.00000
Epoch: 0026 train_loss= 0.69341 train_acc= 0.48788 val_loss= 0.69388 val_acc= 0.47541 time= 0.00000
Epoch: 0027 train_loss= 0.69346 train_acc= 0.48788 val_loss= 0.69385 val_acc= 0.47541 time= 0.01563
Epoch: 0028 train_loss= 0.69334 train_acc= 0.48788 val_loss= 0.69381 val_acc= 0.47541 time= 0.00000
Epoch: 0029 train_loss= 0.69330 train_acc= 0.48788 val_loss= 0.69385 val_acc= 0.47541 time= 0.01563
Epoch: 0030 train_loss= 0.69326 train_acc= 0.48788 val_loss= 0.69387 val_acc= 0.47541 time= 0.01563
Epoch: 0031 train_loss= 0.69328 train_acc= 0.48788 val_loss= 0.69384 val_acc= 0.47541 time= 0.00000
Epoch: 0032 train_loss= 0.69327 train_acc= 0.48788 val_loss= 0.69383 val_acc= 0.47541 time= 0.01563
Epoch: 0033 train_loss= 0.69322 train_acc= 0.48788 val_loss= 0.69382 val_acc= 0.47541 time= 0.00000
Epoch: 0034 train_loss= 0.69320 train_acc= 0.48485 val_loss= 0.69381 val_acc= 0.47541 time= 0.01563
Epoch: 0035 train_loss= 0.69318 train_acc= 0.49394 val_loss= 0.69381 val_acc= 0.47541 time= 0.01563
Epoch: 0036 train_loss= 0.69319 train_acc= 0.49091 val_loss= 0.69377 val_acc= 0.47541 time= 0.01563
Epoch: 0037 train_loss= 0.69314 train_acc= 0.48788 val_loss= 0.69374 val_acc= 0.47541 time= 0.00000
Epoch: 0038 train_loss= 0.69334 train_acc= 0.48788 val_loss= 0.69365 val_acc= 0.47541 time= 0.01563
Epoch: 0039 train_loss= 0.69320 train_acc= 0.49091 val_loss= 0.69354 val_acc= 0.47541 time= 0.00000
Epoch: 0040 train_loss= 0.69320 train_acc= 0.48182 val_loss= 0.69343 val_acc= 0.47541 time= 0.01562
Epoch: 0041 train_loss= 0.69312 train_acc= 0.48788 val_loss= 0.69343 val_acc= 0.47541 time= 0.01563
Epoch: 0042 train_loss= 0.69320 train_acc= 0.46970 val_loss= 0.69342 val_acc= 0.47541 time= 0.00000
Epoch: 0043 train_loss= 0.69312 train_acc= 0.51818 val_loss= 0.69344 val_acc= 0.47541 time= 0.01562
Epoch: 0044 train_loss= 0.69309 train_acc= 0.49394 val_loss= 0.69349 val_acc= 0.47541 time= 0.01563
Epoch: 0045 train_loss= 0.69312 train_acc= 0.49697 val_loss= 0.69353 val_acc= 0.47541 time= 0.00000
Epoch: 0046 train_loss= 0.69321 train_acc= 0.49091 val_loss= 0.69355 val_acc= 0.47541 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69236 accuracy= 0.55738 time= 0.00000 
