Epoch: 0001 train_loss= 0.69970 train_acc= 0.50545 val_loss= 0.69872 val_acc= 0.50820 time= 0.34508
Epoch: 0002 train_loss= 0.69893 train_acc= 0.51091 val_loss= 0.69817 val_acc= 0.50820 time= 0.01200
Epoch: 0003 train_loss= 0.69804 train_acc= 0.51455 val_loss= 0.69769 val_acc= 0.50820 time= 0.01100
Epoch: 0004 train_loss= 0.69687 train_acc= 0.51455 val_loss= 0.69728 val_acc= 0.50820 time= 0.01200
Epoch: 0005 train_loss= 0.69708 train_acc= 0.51455 val_loss= 0.69693 val_acc= 0.50820 time= 0.01500
Epoch: 0006 train_loss= 0.69635 train_acc= 0.51455 val_loss= 0.69664 val_acc= 0.50820 time= 0.01300
Epoch: 0007 train_loss= 0.69608 train_acc= 0.51273 val_loss= 0.69640 val_acc= 0.50820 time= 0.01400
Epoch: 0008 train_loss= 0.69561 train_acc= 0.51273 val_loss= 0.69617 val_acc= 0.50820 time= 0.01100
Epoch: 0009 train_loss= 0.69549 train_acc= 0.51273 val_loss= 0.69592 val_acc= 0.50820 time= 0.01200
Epoch: 0010 train_loss= 0.69573 train_acc= 0.51273 val_loss= 0.69561 val_acc= 0.50820 time= 0.01300
Epoch: 0011 train_loss= 0.69472 train_acc= 0.51273 val_loss= 0.69530 val_acc= 0.50820 time= 0.01500
Epoch: 0012 train_loss= 0.69497 train_acc= 0.51455 val_loss= 0.69499 val_acc= 0.50820 time= 0.01300
Epoch: 0013 train_loss= 0.69460 train_acc= 0.51273 val_loss= 0.69469 val_acc= 0.50820 time= 0.01200
Epoch: 0014 train_loss= 0.69397 train_acc= 0.50909 val_loss= 0.69445 val_acc= 0.50820 time= 0.01200
Epoch: 0015 train_loss= 0.69436 train_acc= 0.51273 val_loss= 0.69423 val_acc= 0.50820 time= 0.01100
Epoch: 0016 train_loss= 0.69415 train_acc= 0.51273 val_loss= 0.69405 val_acc= 0.50820 time= 0.01300
Epoch: 0017 train_loss= 0.69384 train_acc= 0.51636 val_loss= 0.69388 val_acc= 0.50820 time= 0.01100
Epoch: 0018 train_loss= 0.69375 train_acc= 0.51091 val_loss= 0.69375 val_acc= 0.50820 time= 0.01300
Epoch: 0019 train_loss= 0.69352 train_acc= 0.51636 val_loss= 0.69363 val_acc= 0.50820 time= 0.01300
Epoch: 0020 train_loss= 0.69340 train_acc= 0.51273 val_loss= 0.69353 val_acc= 0.50820 time= 0.01600
Epoch: 0021 train_loss= 0.69334 train_acc= 0.51273 val_loss= 0.69343 val_acc= 0.50820 time= 0.01100
Epoch: 0022 train_loss= 0.69326 train_acc= 0.51091 val_loss= 0.69335 val_acc= 0.50820 time= 0.01300
Epoch: 0023 train_loss= 0.69319 train_acc= 0.51273 val_loss= 0.69328 val_acc= 0.50820 time= 0.01200
Epoch: 0024 train_loss= 0.69315 train_acc= 0.51455 val_loss= 0.69323 val_acc= 0.50820 time= 0.01200
Epoch: 0025 train_loss= 0.69281 train_acc= 0.51273 val_loss= 0.69320 val_acc= 0.50820 time= 0.01100
Epoch: 0026 train_loss= 0.69309 train_acc= 0.51273 val_loss= 0.69317 val_acc= 0.50820 time= 0.01300
Epoch: 0027 train_loss= 0.69322 train_acc= 0.51273 val_loss= 0.69313 val_acc= 0.50820 time= 0.01300
Epoch: 0028 train_loss= 0.69234 train_acc= 0.51455 val_loss= 0.69311 val_acc= 0.50820 time= 0.01100
Epoch: 0029 train_loss= 0.69233 train_acc= 0.51636 val_loss= 0.69309 val_acc= 0.50820 time= 0.01100
Epoch: 0030 train_loss= 0.69297 train_acc= 0.51273 val_loss= 0.69308 val_acc= 0.50820 time= 0.01200
Epoch: 0031 train_loss= 0.69324 train_acc= 0.51273 val_loss= 0.69305 val_acc= 0.50820 time= 0.01300
Epoch: 0032 train_loss= 0.69255 train_acc= 0.51273 val_loss= 0.69302 val_acc= 0.50820 time= 0.01100
Epoch: 0033 train_loss= 0.69289 train_acc= 0.51273 val_loss= 0.69299 val_acc= 0.50820 time= 0.01400
Epoch: 0034 train_loss= 0.69266 train_acc= 0.51273 val_loss= 0.69296 val_acc= 0.50820 time= 0.01100
Epoch: 0035 train_loss= 0.69275 train_acc= 0.51273 val_loss= 0.69294 val_acc= 0.50820 time= 0.01100
Epoch: 0036 train_loss= 0.69237 train_acc= 0.51455 val_loss= 0.69292 val_acc= 0.50820 time= 0.01000
Epoch: 0037 train_loss= 0.69252 train_acc= 0.51273 val_loss= 0.69291 val_acc= 0.50820 time= 0.01200
Epoch: 0038 train_loss= 0.69214 train_acc= 0.51273 val_loss= 0.69292 val_acc= 0.50820 time= 0.01200
Epoch: 0039 train_loss= 0.69299 train_acc= 0.51273 val_loss= 0.69291 val_acc= 0.50820 time= 0.01300
Epoch: 0040 train_loss= 0.69289 train_acc= 0.51273 val_loss= 0.69290 val_acc= 0.50820 time= 0.01200
Epoch: 0041 train_loss= 0.69270 train_acc= 0.51273 val_loss= 0.69289 val_acc= 0.50820 time= 0.01200
Epoch: 0042 train_loss= 0.69276 train_acc= 0.51273 val_loss= 0.69288 val_acc= 0.50820 time= 0.01300
Epoch: 0043 train_loss= 0.69276 train_acc= 0.51273 val_loss= 0.69287 val_acc= 0.50820 time= 0.01300
Epoch: 0044 train_loss= 0.69300 train_acc= 0.51273 val_loss= 0.69286 val_acc= 0.50820 time= 0.01200
Epoch: 0045 train_loss= 0.69273 train_acc= 0.51273 val_loss= 0.69284 val_acc= 0.50820 time= 0.01200
Epoch: 0046 train_loss= 0.69263 train_acc= 0.51455 val_loss= 0.69284 val_acc= 0.50820 time= 0.01000
Epoch: 0047 train_loss= 0.69247 train_acc= 0.51273 val_loss= 0.69284 val_acc= 0.50820 time= 0.01200
Epoch: 0048 train_loss= 0.69268 train_acc= 0.51455 val_loss= 0.69284 val_acc= 0.50820 time= 0.01300
Epoch: 0049 train_loss= 0.69271 train_acc= 0.51273 val_loss= 0.69284 val_acc= 0.50820 time= 0.01100
Epoch: 0050 train_loss= 0.69269 train_acc= 0.51273 val_loss= 0.69284 val_acc= 0.50820 time= 0.01100
Epoch: 0051 train_loss= 0.69259 train_acc= 0.51273 val_loss= 0.69285 val_acc= 0.50820 time= 0.01000
Epoch: 0052 train_loss= 0.69253 train_acc= 0.51273 val_loss= 0.69286 val_acc= 0.50820 time= 0.01200
Early stopping...
Optimization Finished!
Test set results: cost= 0.68821 accuracy= 0.55738 time= 0.00500 
