Epoch: 0001 train_loss= 0.70102 train_acc= 0.49091 val_loss= 0.69799 val_acc= 0.63934 time= 0.35152
Epoch: 0002 train_loss= 0.69812 train_acc= 0.53377 val_loss= 0.69570 val_acc= 0.57377 time= 0.01563
Epoch: 0003 train_loss= 0.69589 train_acc= 0.55195 val_loss= 0.69419 val_acc= 0.59016 time= 0.00000
Epoch: 0004 train_loss= 0.69442 train_acc= 0.60260 val_loss= 0.69328 val_acc= 0.59016 time= 0.01563
Epoch: 0005 train_loss= 0.69339 train_acc= 0.64156 val_loss= 0.69290 val_acc= 0.65574 time= 0.01563
Epoch: 0006 train_loss= 0.69282 train_acc= 0.62208 val_loss= 0.69284 val_acc= 0.54098 time= 0.00000
Epoch: 0007 train_loss= 0.69271 train_acc= 0.55195 val_loss= 0.69286 val_acc= 0.54098 time= 0.01563
Epoch: 0008 train_loss= 0.69230 train_acc= 0.59870 val_loss= 0.69284 val_acc= 0.54098 time= 0.01563
Epoch: 0009 train_loss= 0.69243 train_acc= 0.57273 val_loss= 0.69265 val_acc= 0.65574 time= 0.01563
Epoch: 0010 train_loss= 0.69239 train_acc= 0.61558 val_loss= 0.69235 val_acc= 0.63934 time= 0.00000
Epoch: 0011 train_loss= 0.69244 train_acc= 0.61818 val_loss= 0.69201 val_acc= 0.59016 time= 0.01563
Epoch: 0012 train_loss= 0.69207 train_acc= 0.64026 val_loss= 0.69163 val_acc= 0.60656 time= 0.01563
Epoch: 0013 train_loss= 0.69271 train_acc= 0.59221 val_loss= 0.69146 val_acc= 0.60656 time= 0.01563
Epoch: 0014 train_loss= 0.69213 train_acc= 0.60130 val_loss= 0.69149 val_acc= 0.65574 time= 0.00000
Epoch: 0015 train_loss= 0.69138 train_acc= 0.62468 val_loss= 0.69145 val_acc= 0.62295 time= 0.01562
Epoch: 0016 train_loss= 0.69107 train_acc= 0.61558 val_loss= 0.69130 val_acc= 0.65574 time= 0.01563
Epoch: 0017 train_loss= 0.69118 train_acc= 0.60909 val_loss= 0.69094 val_acc= 0.60656 time= 0.00000
Epoch: 0018 train_loss= 0.69017 train_acc= 0.62857 val_loss= 0.69045 val_acc= 0.65574 time= 0.01563
Epoch: 0019 train_loss= 0.69046 train_acc= 0.62727 val_loss= 0.68995 val_acc= 0.63934 time= 0.01562
Epoch: 0020 train_loss= 0.69039 train_acc= 0.63507 val_loss= 0.68970 val_acc= 0.62295 time= 0.00000
Epoch: 0021 train_loss= 0.69004 train_acc= 0.63896 val_loss= 0.68959 val_acc= 0.65574 time= 0.01563
Epoch: 0022 train_loss= 0.68924 train_acc= 0.65065 val_loss= 0.68971 val_acc= 0.60656 time= 0.01563
Epoch: 0023 train_loss= 0.68991 train_acc= 0.63377 val_loss= 0.68954 val_acc= 0.62295 time= 0.01563
Epoch: 0024 train_loss= 0.68919 train_acc= 0.63636 val_loss= 0.68907 val_acc= 0.67213 time= 0.00000
Epoch: 0025 train_loss= 0.68914 train_acc= 0.62987 val_loss= 0.68845 val_acc= 0.62295 time= 0.01563
Epoch: 0026 train_loss= 0.68955 train_acc= 0.60909 val_loss= 0.68768 val_acc= 0.60656 time= 0.01563
Epoch: 0027 train_loss= 0.68839 train_acc= 0.62208 val_loss= 0.68737 val_acc= 0.60656 time= 0.00000
Epoch: 0028 train_loss= 0.68913 train_acc= 0.63766 val_loss= 0.68711 val_acc= 0.60656 time= 0.01563
Epoch: 0029 train_loss= 0.68828 train_acc= 0.61429 val_loss= 0.68721 val_acc= 0.62295 time= 0.01563
Epoch: 0030 train_loss= 0.68864 train_acc= 0.63247 val_loss= 0.68767 val_acc= 0.62295 time= 0.00000
Epoch: 0031 train_loss= 0.68860 train_acc= 0.62727 val_loss= 0.68790 val_acc= 0.63934 time= 0.00000
Epoch: 0032 train_loss= 0.68827 train_acc= 0.59870 val_loss= 0.68727 val_acc= 0.60656 time= 0.01563
Epoch: 0033 train_loss= 0.68864 train_acc= 0.62208 val_loss= 0.68622 val_acc= 0.63934 time= 0.01563
Epoch: 0034 train_loss= 0.68740 train_acc= 0.62338 val_loss= 0.68571 val_acc= 0.60656 time= 0.00000
Epoch: 0035 train_loss= 0.68877 train_acc= 0.64935 val_loss= 0.68547 val_acc= 0.60656 time= 0.01563
Epoch: 0036 train_loss= 0.68622 train_acc= 0.61688 val_loss= 0.68559 val_acc= 0.65574 time= 0.01563
Epoch: 0037 train_loss= 0.68631 train_acc= 0.65455 val_loss= 0.68558 val_acc= 0.65574 time= 0.00000
Epoch: 0038 train_loss= 0.68734 train_acc= 0.62338 val_loss= 0.68545 val_acc= 0.67213 time= 0.01563
Epoch: 0039 train_loss= 0.68758 train_acc= 0.62338 val_loss= 0.68514 val_acc= 0.67213 time= 0.01563
Epoch: 0040 train_loss= 0.68568 train_acc= 0.62727 val_loss= 0.68546 val_acc= 0.62295 time= 0.01563
Epoch: 0041 train_loss= 0.68627 train_acc= 0.62208 val_loss= 0.68549 val_acc= 0.63934 time= 0.00000
Epoch: 0042 train_loss= 0.68521 train_acc= 0.58312 val_loss= 0.68392 val_acc= 0.63934 time= 0.01563
Epoch: 0043 train_loss= 0.68445 train_acc= 0.62597 val_loss= 0.68287 val_acc= 0.59016 time= 0.01563
Epoch: 0044 train_loss= 0.68591 train_acc= 0.62468 val_loss= 0.68256 val_acc= 0.59016 time= 0.00000
Epoch: 0045 train_loss= 0.68603 train_acc= 0.56883 val_loss= 0.68258 val_acc= 0.60656 time= 0.01563
Epoch: 0046 train_loss= 0.68535 train_acc= 0.63766 val_loss= 0.68304 val_acc= 0.63934 time= 0.01563
Epoch: 0047 train_loss= 0.68367 train_acc= 0.61169 val_loss= 0.68250 val_acc= 0.65574 time= 0.00000
Epoch: 0048 train_loss= 0.68258 train_acc= 0.64286 val_loss= 0.68199 val_acc= 0.62295 time= 0.01563
Epoch: 0049 train_loss= 0.68358 train_acc= 0.61169 val_loss= 0.68139 val_acc= 0.60656 time= 0.01563
Epoch: 0050 train_loss= 0.68266 train_acc= 0.64675 val_loss= 0.68116 val_acc= 0.60656 time= 0.00000
Epoch: 0051 train_loss= 0.68291 train_acc= 0.60909 val_loss= 0.68157 val_acc= 0.63934 time= 0.01563
Epoch: 0052 train_loss= 0.68277 train_acc= 0.62468 val_loss= 0.68230 val_acc= 0.62295 time= 0.01563
Epoch: 0053 train_loss= 0.68248 train_acc= 0.63896 val_loss= 0.68286 val_acc= 0.62295 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.68686 accuracy= 0.71311 time= 0.00000 
