Epoch: 0001 train_loss= 0.69940 train_acc= 0.49697 val_loss= 0.69883 val_acc= 0.49180 time= 0.09376
Epoch: 0002 train_loss= 0.69885 train_acc= 0.50606 val_loss= 0.69821 val_acc= 0.50820 time= 0.01563
Epoch: 0003 train_loss= 0.69810 train_acc= 0.53030 val_loss= 0.69769 val_acc= 0.50820 time= 0.01563
Epoch: 0004 train_loss= 0.69791 train_acc= 0.50303 val_loss= 0.69728 val_acc= 0.50820 time= 0.01563
Epoch: 0005 train_loss= 0.69706 train_acc= 0.49697 val_loss= 0.69689 val_acc= 0.50820 time= 0.00000
Epoch: 0006 train_loss= 0.69700 train_acc= 0.50000 val_loss= 0.69654 val_acc= 0.50820 time= 0.01563
Epoch: 0007 train_loss= 0.69669 train_acc= 0.52424 val_loss= 0.69621 val_acc= 0.50820 time= 0.01563
Epoch: 0008 train_loss= 0.69679 train_acc= 0.48485 val_loss= 0.69590 val_acc= 0.50820 time= 0.00000
Epoch: 0009 train_loss= 0.69591 train_acc= 0.49697 val_loss= 0.69563 val_acc= 0.50820 time= 0.01563
Epoch: 0010 train_loss= 0.69577 train_acc= 0.48788 val_loss= 0.69534 val_acc= 0.50820 time= 0.00000
Epoch: 0011 train_loss= 0.69546 train_acc= 0.51818 val_loss= 0.69509 val_acc= 0.50820 time= 0.01563
Epoch: 0012 train_loss= 0.69533 train_acc= 0.50606 val_loss= 0.69482 val_acc= 0.50820 time= 0.01563
Epoch: 0013 train_loss= 0.69467 train_acc= 0.50000 val_loss= 0.69458 val_acc= 0.50820 time= 0.00000
Epoch: 0014 train_loss= 0.69467 train_acc= 0.50000 val_loss= 0.69434 val_acc= 0.50820 time= 0.01563
Epoch: 0015 train_loss= 0.69458 train_acc= 0.48788 val_loss= 0.69415 val_acc= 0.50820 time= 0.00000
Epoch: 0016 train_loss= 0.69448 train_acc= 0.50909 val_loss= 0.69405 val_acc= 0.50820 time= 0.01563
Epoch: 0017 train_loss= 0.69386 train_acc= 0.51212 val_loss= 0.69396 val_acc= 0.50820 time= 0.01563
Epoch: 0018 train_loss= 0.69441 train_acc= 0.49697 val_loss= 0.69389 val_acc= 0.50820 time= 0.00000
Epoch: 0019 train_loss= 0.69401 train_acc= 0.50909 val_loss= 0.69380 val_acc= 0.50820 time= 0.01563
Epoch: 0020 train_loss= 0.69384 train_acc= 0.50909 val_loss= 0.69370 val_acc= 0.50820 time= 0.01563
Epoch: 0021 train_loss= 0.69370 train_acc= 0.50000 val_loss= 0.69361 val_acc= 0.50820 time= 0.00000
Epoch: 0022 train_loss= 0.69383 train_acc= 0.47879 val_loss= 0.69355 val_acc= 0.50820 time= 0.01563
Epoch: 0023 train_loss= 0.69378 train_acc= 0.50909 val_loss= 0.69353 val_acc= 0.50820 time= 0.01563
Epoch: 0024 train_loss= 0.69349 train_acc= 0.50000 val_loss= 0.69354 val_acc= 0.50820 time= 0.00000
Epoch: 0025 train_loss= 0.69396 train_acc= 0.49091 val_loss= 0.69355 val_acc= 0.49180 time= 0.01563
Epoch: 0026 train_loss= 0.69381 train_acc= 0.48788 val_loss= 0.69361 val_acc= 0.49180 time= 0.01563
Epoch: 0027 train_loss= 0.69341 train_acc= 0.51515 val_loss= 0.69364 val_acc= 0.49180 time= 0.01147
Epoch: 0028 train_loss= 0.69348 train_acc= 0.49091 val_loss= 0.69363 val_acc= 0.49180 time= 0.01372
Epoch: 0029 train_loss= 0.69328 train_acc= 0.51212 val_loss= 0.69356 val_acc= 0.49180 time= 0.00808
Epoch: 0030 train_loss= 0.69354 train_acc= 0.49091 val_loss= 0.69349 val_acc= 0.49180 time= 0.01859
Epoch: 0031 train_loss= 0.69311 train_acc= 0.52424 val_loss= 0.69341 val_acc= 0.49180 time= 0.01300
Epoch: 0032 train_loss= 0.69344 train_acc= 0.50303 val_loss= 0.69332 val_acc= 0.50820 time= 0.01000
Epoch: 0033 train_loss= 0.69331 train_acc= 0.50909 val_loss= 0.69320 val_acc= 0.50820 time= 0.01200
Epoch: 0034 train_loss= 0.69360 train_acc= 0.48485 val_loss= 0.69314 val_acc= 0.50820 time= 0.01000
Epoch: 0035 train_loss= 0.69345 train_acc= 0.50909 val_loss= 0.69309 val_acc= 0.50820 time= 0.00900
Epoch: 0036 train_loss= 0.69341 train_acc= 0.49697 val_loss= 0.69308 val_acc= 0.50820 time= 0.01000
Epoch: 0037 train_loss= 0.69355 train_acc= 0.49091 val_loss= 0.69311 val_acc= 0.50820 time= 0.01000
Epoch: 0038 train_loss= 0.69374 train_acc= 0.47879 val_loss= 0.69314 val_acc= 0.50820 time= 0.01100
Epoch: 0039 train_loss= 0.69293 train_acc= 0.55152 val_loss= 0.69319 val_acc= 0.50820 time= 0.01200
Epoch: 0040 train_loss= 0.69330 train_acc= 0.50000 val_loss= 0.69319 val_acc= 0.50820 time= 0.01100
Epoch: 0041 train_loss= 0.69344 train_acc= 0.45758 val_loss= 0.69319 val_acc= 0.50820 time= 0.01200
Early stopping...
Optimization Finished!
Test set results: cost= 0.69335 accuracy= 0.48361 time= 0.00500 
