Epoch: 0001 train_loss= 0.69920 train_acc= 0.49636 val_loss= 0.69955 val_acc= 0.44262 time= 0.23438
Epoch: 0002 train_loss= 0.69865 train_acc= 0.49273 val_loss= 0.69899 val_acc= 0.44262 time= 0.00000
Epoch: 0003 train_loss= 0.69812 train_acc= 0.49818 val_loss= 0.69840 val_acc= 0.44262 time= 0.01562
Epoch: 0004 train_loss= 0.69763 train_acc= 0.49273 val_loss= 0.69787 val_acc= 0.44262 time= 0.01563
Epoch: 0005 train_loss= 0.69714 train_acc= 0.49091 val_loss= 0.69741 val_acc= 0.44262 time= 0.01563
Epoch: 0006 train_loss= 0.69672 train_acc= 0.49636 val_loss= 0.69698 val_acc= 0.44262 time= 0.01563
Epoch: 0007 train_loss= 0.69632 train_acc= 0.49636 val_loss= 0.69654 val_acc= 0.44262 time= 0.00000
Epoch: 0008 train_loss= 0.69591 train_acc= 0.49273 val_loss= 0.69620 val_acc= 0.44262 time= 0.01563
Epoch: 0009 train_loss= 0.69566 train_acc= 0.49273 val_loss= 0.69592 val_acc= 0.44262 time= 0.01563
Epoch: 0010 train_loss= 0.69531 train_acc= 0.49818 val_loss= 0.69568 val_acc= 0.44262 time= 0.01563
Epoch: 0011 train_loss= 0.69503 train_acc= 0.49455 val_loss= 0.69548 val_acc= 0.44262 time= 0.01563
Epoch: 0012 train_loss= 0.69482 train_acc= 0.49273 val_loss= 0.69528 val_acc= 0.44262 time= 0.01563
Epoch: 0013 train_loss= 0.69463 train_acc= 0.49636 val_loss= 0.69503 val_acc= 0.44262 time= 0.00000
Epoch: 0014 train_loss= 0.69437 train_acc= 0.49273 val_loss= 0.69478 val_acc= 0.44262 time= 0.01563
Epoch: 0015 train_loss= 0.69423 train_acc= 0.50000 val_loss= 0.69445 val_acc= 0.44262 time= 0.01563
Epoch: 0016 train_loss= 0.69410 train_acc= 0.49818 val_loss= 0.69419 val_acc= 0.44262 time= 0.01563
Epoch: 0017 train_loss= 0.69393 train_acc= 0.49818 val_loss= 0.69408 val_acc= 0.44262 time= 0.01563
Epoch: 0018 train_loss= 0.69385 train_acc= 0.49818 val_loss= 0.69397 val_acc= 0.44262 time= 0.00000
Epoch: 0019 train_loss= 0.69373 train_acc= 0.49636 val_loss= 0.69386 val_acc= 0.44262 time= 0.01563
Epoch: 0020 train_loss= 0.69359 train_acc= 0.49091 val_loss= 0.69390 val_acc= 0.44262 time= 0.01563
Epoch: 0021 train_loss= 0.69351 train_acc= 0.49818 val_loss= 0.69396 val_acc= 0.44262 time= 0.01563
Epoch: 0022 train_loss= 0.69346 train_acc= 0.49455 val_loss= 0.69404 val_acc= 0.44262 time= 0.01563
Epoch: 0023 train_loss= 0.69343 train_acc= 0.49455 val_loss= 0.69404 val_acc= 0.44262 time= 0.00000
Epoch: 0024 train_loss= 0.69339 train_acc= 0.49455 val_loss= 0.69399 val_acc= 0.44262 time= 0.01562
Epoch: 0025 train_loss= 0.69331 train_acc= 0.49455 val_loss= 0.69397 val_acc= 0.44262 time= 0.01563
Epoch: 0026 train_loss= 0.69330 train_acc= 0.49455 val_loss= 0.69394 val_acc= 0.44262 time= 0.01563
Epoch: 0027 train_loss= 0.69326 train_acc= 0.49455 val_loss= 0.69389 val_acc= 0.44262 time= 0.01563
Epoch: 0028 train_loss= 0.69328 train_acc= 0.49455 val_loss= 0.69379 val_acc= 0.44262 time= 0.00000
Epoch: 0029 train_loss= 0.69322 train_acc= 0.49455 val_loss= 0.69369 val_acc= 0.44262 time= 0.01563
Epoch: 0030 train_loss= 0.69323 train_acc= 0.49455 val_loss= 0.69355 val_acc= 0.44262 time= 0.01563
Epoch: 0031 train_loss= 0.69319 train_acc= 0.49273 val_loss= 0.69351 val_acc= 0.44262 time= 0.02072
Epoch: 0032 train_loss= 0.69321 train_acc= 0.49455 val_loss= 0.69345 val_acc= 0.44262 time= 0.00309
Epoch: 0033 train_loss= 0.69321 train_acc= 0.49636 val_loss= 0.69334 val_acc= 0.44262 time= 0.01563
Epoch: 0034 train_loss= 0.69321 train_acc= 0.49818 val_loss= 0.69333 val_acc= 0.44262 time= 0.01563
Epoch: 0035 train_loss= 0.69318 train_acc= 0.49273 val_loss= 0.69329 val_acc= 0.44262 time= 0.01563
Epoch: 0036 train_loss= 0.69318 train_acc= 0.50364 val_loss= 0.69338 val_acc= 0.44262 time= 0.00000
Epoch: 0037 train_loss= 0.69317 train_acc= 0.49818 val_loss= 0.69349 val_acc= 0.44262 time= 0.02864
Epoch: 0038 train_loss= 0.69317 train_acc= 0.49455 val_loss= 0.69368 val_acc= 0.44262 time= 0.00800
Early stopping...
Optimization Finished!
Test set results: cost= 0.69336 accuracy= 0.45902 time= 0.00000 
