Epoch: 0001 train_loss= 0.69777 train_acc= 0.52078 val_loss= 0.69693 val_acc= 0.55738 time= 0.70322
Epoch: 0002 train_loss= 0.69766 train_acc= 0.51299 val_loss= 0.69630 val_acc= 0.55738 time= 0.01562
Epoch: 0003 train_loss= 0.69729 train_acc= 0.51429 val_loss= 0.69578 val_acc= 0.55738 time= 0.01563
Epoch: 0004 train_loss= 0.69623 train_acc= 0.51818 val_loss= 0.69528 val_acc= 0.55738 time= 0.01563
Epoch: 0005 train_loss= 0.69592 train_acc= 0.51429 val_loss= 0.69484 val_acc= 0.55738 time= 0.01563
Epoch: 0006 train_loss= 0.69587 train_acc= 0.51429 val_loss= 0.69444 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 0.69623 train_acc= 0.51299 val_loss= 0.69411 val_acc= 0.55738 time= 0.00000
Epoch: 0008 train_loss= 0.69532 train_acc= 0.51429 val_loss= 0.69381 val_acc= 0.55738 time= 0.01563
Epoch: 0009 train_loss= 0.69473 train_acc= 0.51429 val_loss= 0.69355 val_acc= 0.55738 time= 0.01562
Epoch: 0010 train_loss= 0.69472 train_acc= 0.51429 val_loss= 0.69332 val_acc= 0.55738 time= 0.01563
Epoch: 0011 train_loss= 0.69364 train_acc= 0.51429 val_loss= 0.69310 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 0.69396 train_acc= 0.51429 val_loss= 0.69291 val_acc= 0.55738 time= 0.01563
Epoch: 0013 train_loss= 0.69420 train_acc= 0.51429 val_loss= 0.69274 val_acc= 0.55738 time= 0.00000
Epoch: 0014 train_loss= 0.69409 train_acc= 0.51429 val_loss= 0.69260 val_acc= 0.55738 time= 0.01563
Epoch: 0015 train_loss= 0.69322 train_acc= 0.51429 val_loss= 0.69247 val_acc= 0.55738 time= 0.01563
Epoch: 0016 train_loss= 0.69339 train_acc= 0.51429 val_loss= 0.69235 val_acc= 0.55738 time= 0.01562
Epoch: 0017 train_loss= 0.69338 train_acc= 0.51429 val_loss= 0.69224 val_acc= 0.55738 time= 0.01563
Epoch: 0018 train_loss= 0.69316 train_acc= 0.51299 val_loss= 0.69215 val_acc= 0.55738 time= 0.00000
Epoch: 0019 train_loss= 0.69278 train_acc= 0.51429 val_loss= 0.69205 val_acc= 0.55738 time= 0.01563
Epoch: 0020 train_loss= 0.69313 train_acc= 0.51429 val_loss= 0.69198 val_acc= 0.55738 time= 0.01562
Epoch: 0021 train_loss= 0.69297 train_acc= 0.51299 val_loss= 0.69192 val_acc= 0.55738 time= 0.01563
Epoch: 0022 train_loss= 0.69310 train_acc= 0.51429 val_loss= 0.69188 val_acc= 0.55738 time= 0.01562
Epoch: 0023 train_loss= 0.69253 train_acc= 0.51429 val_loss= 0.69183 val_acc= 0.55738 time= 0.01563
Epoch: 0024 train_loss= 0.69216 train_acc= 0.51558 val_loss= 0.69177 val_acc= 0.55738 time= 0.00000
Epoch: 0025 train_loss= 0.69272 train_acc= 0.51429 val_loss= 0.69168 val_acc= 0.55738 time= 0.01563
Epoch: 0026 train_loss= 0.69303 train_acc= 0.51429 val_loss= 0.69162 val_acc= 0.55738 time= 0.01563
Epoch: 0027 train_loss= 0.69260 train_acc= 0.51429 val_loss= 0.69157 val_acc= 0.55738 time= 0.01563
Epoch: 0028 train_loss= 0.69271 train_acc= 0.51429 val_loss= 0.69152 val_acc= 0.55738 time= 0.01563
Epoch: 0029 train_loss= 0.69235 train_acc= 0.51429 val_loss= 0.69146 val_acc= 0.55738 time= 0.01563
Epoch: 0030 train_loss= 0.69243 train_acc= 0.51429 val_loss= 0.69143 val_acc= 0.55738 time= 0.00000
Epoch: 0031 train_loss= 0.69255 train_acc= 0.51429 val_loss= 0.69141 val_acc= 0.55738 time= 0.01563
Epoch: 0032 train_loss= 0.69227 train_acc= 0.51429 val_loss= 0.69139 val_acc= 0.55738 time= 0.01562
Epoch: 0033 train_loss= 0.69262 train_acc= 0.51429 val_loss= 0.69142 val_acc= 0.55738 time= 0.01563
Epoch: 0034 train_loss= 0.69248 train_acc= 0.51429 val_loss= 0.69146 val_acc= 0.55738 time= 0.01562
Epoch: 0035 train_loss= 0.69275 train_acc= 0.51429 val_loss= 0.69151 val_acc= 0.55738 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68948 accuracy= 0.54918 time= 0.00000 
