Epoch: 0001 train_loss= 0.69923 train_acc= 0.51169 val_loss= 0.69702 val_acc= 0.63934 time= 0.32279
Epoch: 0002 train_loss= 0.69856 train_acc= 0.51169 val_loss= 0.69678 val_acc= 0.63934 time= 0.01400
Epoch: 0003 train_loss= 0.69807 train_acc= 0.51169 val_loss= 0.69665 val_acc= 0.63934 time= 0.01248
Epoch: 0004 train_loss= 0.69751 train_acc= 0.51429 val_loss= 0.69646 val_acc= 0.63934 time= 0.01400
Epoch: 0005 train_loss= 0.69707 train_acc= 0.51169 val_loss= 0.69622 val_acc= 0.63934 time= 0.01369
Epoch: 0006 train_loss= 0.69666 train_acc= 0.51299 val_loss= 0.69581 val_acc= 0.63934 time= 0.00800
Epoch: 0007 train_loss= 0.69631 train_acc= 0.49740 val_loss= 0.69530 val_acc= 0.63934 time= 0.01567
Epoch: 0008 train_loss= 0.69589 train_acc= 0.51169 val_loss= 0.69487 val_acc= 0.63934 time= 0.01563
Epoch: 0009 train_loss= 0.69561 train_acc= 0.51429 val_loss= 0.69441 val_acc= 0.63934 time= 0.00000
Epoch: 0010 train_loss= 0.69526 train_acc= 0.51688 val_loss= 0.69409 val_acc= 0.63934 time= 0.01563
Epoch: 0011 train_loss= 0.69503 train_acc= 0.51429 val_loss= 0.69373 val_acc= 0.63934 time= 0.01563
Epoch: 0012 train_loss= 0.69476 train_acc= 0.51169 val_loss= 0.69349 val_acc= 0.63934 time= 0.01563
Epoch: 0013 train_loss= 0.69455 train_acc= 0.51169 val_loss= 0.69331 val_acc= 0.63934 time= 0.01563
Epoch: 0014 train_loss= 0.69439 train_acc= 0.51039 val_loss= 0.69362 val_acc= 0.63934 time= 0.01563
Epoch: 0015 train_loss= 0.69420 train_acc= 0.51039 val_loss= 0.69379 val_acc= 0.63934 time= 0.00000
Epoch: 0016 train_loss= 0.69406 train_acc= 0.51429 val_loss= 0.69381 val_acc= 0.63934 time= 0.01563
Epoch: 0017 train_loss= 0.69395 train_acc= 0.47143 val_loss= 0.69369 val_acc= 0.63934 time= 0.01563
Epoch: 0018 train_loss= 0.69382 train_acc= 0.49870 val_loss= 0.69329 val_acc= 0.63934 time= 0.01563
Epoch: 0019 train_loss= 0.69370 train_acc= 0.51039 val_loss= 0.69295 val_acc= 0.63934 time= 0.01563
Epoch: 0020 train_loss= 0.69363 train_acc= 0.50909 val_loss= 0.69281 val_acc= 0.63934 time= 0.01563
Epoch: 0021 train_loss= 0.69355 train_acc= 0.51169 val_loss= 0.69277 val_acc= 0.63934 time= 0.01563
Epoch: 0022 train_loss= 0.69348 train_acc= 0.51169 val_loss= 0.69279 val_acc= 0.63934 time= 0.00000
Epoch: 0023 train_loss= 0.69341 train_acc= 0.51039 val_loss= 0.69277 val_acc= 0.63934 time= 0.01563
Epoch: 0024 train_loss= 0.69337 train_acc= 0.50909 val_loss= 0.69268 val_acc= 0.63934 time= 0.01563
Epoch: 0025 train_loss= 0.69333 train_acc= 0.51039 val_loss= 0.69267 val_acc= 0.63934 time= 0.01563
Epoch: 0026 train_loss= 0.69330 train_acc= 0.51169 val_loss= 0.69260 val_acc= 0.63934 time= 0.01563
Epoch: 0027 train_loss= 0.69328 train_acc= 0.51169 val_loss= 0.69261 val_acc= 0.63934 time= 0.01563
Epoch: 0028 train_loss= 0.69324 train_acc= 0.51169 val_loss= 0.69261 val_acc= 0.63934 time= 0.01563
Epoch: 0029 train_loss= 0.69323 train_acc= 0.51169 val_loss= 0.69258 val_acc= 0.63934 time= 0.00000
Epoch: 0030 train_loss= 0.69322 train_acc= 0.51169 val_loss= 0.69255 val_acc= 0.63934 time= 0.01563
Epoch: 0031 train_loss= 0.69321 train_acc= 0.51169 val_loss= 0.69254 val_acc= 0.63934 time= 0.01563
Epoch: 0032 train_loss= 0.69319 train_acc= 0.51169 val_loss= 0.69252 val_acc= 0.63934 time= 0.01563
Epoch: 0033 train_loss= 0.69319 train_acc= 0.51169 val_loss= 0.69248 val_acc= 0.63934 time= 0.01563
Epoch: 0034 train_loss= 0.69319 train_acc= 0.51169 val_loss= 0.69244 val_acc= 0.63934 time= 0.01563
Epoch: 0035 train_loss= 0.69318 train_acc= 0.51169 val_loss= 0.69243 val_acc= 0.63934 time= 0.00000
Epoch: 0036 train_loss= 0.69318 train_acc= 0.51169 val_loss= 0.69248 val_acc= 0.63934 time= 0.01563
Epoch: 0037 train_loss= 0.69318 train_acc= 0.51169 val_loss= 0.69258 val_acc= 0.63934 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69323 accuracy= 0.48361 time= 0.01563 
