Epoch: 0001 train_loss= 1.39425 train_acc= 0.26710 val_loss= 1.39022 val_acc= 0.35714 time= 0.17189
Epoch: 0002 train_loss= 1.39130 train_acc= 0.27362 val_loss= 1.38691 val_acc= 0.35714 time= 0.00000
Epoch: 0003 train_loss= 1.38897 train_acc= 0.27362 val_loss= 1.38413 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38742 train_acc= 0.27362 val_loss= 1.38189 val_acc= 0.35714 time= 0.01563
Epoch: 0005 train_loss= 1.38623 train_acc= 0.27362 val_loss= 1.38007 val_acc= 0.35714 time= 0.01563
Epoch: 0006 train_loss= 1.38552 train_acc= 0.27362 val_loss= 1.37847 val_acc= 0.35714 time= 0.01562
Epoch: 0007 train_loss= 1.38513 train_acc= 0.27362 val_loss= 1.37711 val_acc= 0.35714 time= 0.01563
Epoch: 0008 train_loss= 1.38482 train_acc= 0.27362 val_loss= 1.37595 val_acc= 0.35714 time= 0.03125
Epoch: 0009 train_loss= 1.38484 train_acc= 0.27362 val_loss= 1.37481 val_acc= 0.35714 time= 0.01563
Epoch: 0010 train_loss= 1.38496 train_acc= 0.27036 val_loss= 1.37378 val_acc= 0.35714 time= 0.01563
Epoch: 0011 train_loss= 1.38496 train_acc= 0.27687 val_loss= 1.37290 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38475 train_acc= 0.27687 val_loss= 1.37207 val_acc= 0.35714 time= 0.01769
Epoch: 0013 train_loss= 1.38458 train_acc= 0.27687 val_loss= 1.37127 val_acc= 0.35714 time= 0.01769
Epoch: 0014 train_loss= 1.38475 train_acc= 0.26059 val_loss= 1.37054 val_acc= 0.35714 time= 0.01562
Epoch: 0015 train_loss= 1.38446 train_acc= 0.28664 val_loss= 1.36995 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.38423 train_acc= 0.27687 val_loss= 1.36943 val_acc= 0.37500 time= 0.01563
Epoch: 0017 train_loss= 1.38395 train_acc= 0.26384 val_loss= 1.36889 val_acc= 0.42857 time= 0.01563
Epoch: 0018 train_loss= 1.38385 train_acc= 0.28339 val_loss= 1.36839 val_acc= 0.46429 time= 0.01563
Epoch: 0019 train_loss= 1.38315 train_acc= 0.32573 val_loss= 1.36791 val_acc= 0.44643 time= 0.01563
Epoch: 0020 train_loss= 1.38344 train_acc= 0.26059 val_loss= 1.36747 val_acc= 0.46429 time= 0.01563
Epoch: 0021 train_loss= 1.38299 train_acc= 0.31596 val_loss= 1.36683 val_acc= 0.50000 time= 0.01563
Epoch: 0022 train_loss= 1.38292 train_acc= 0.31270 val_loss= 1.36615 val_acc= 0.46429 time= 0.01563
Epoch: 0023 train_loss= 1.38299 train_acc= 0.28664 val_loss= 1.36559 val_acc= 0.37500 time= 0.01563
Epoch: 0024 train_loss= 1.38265 train_acc= 0.30293 val_loss= 1.36514 val_acc= 0.39286 time= 0.01563
Epoch: 0025 train_loss= 1.38289 train_acc= 0.27362 val_loss= 1.36471 val_acc= 0.44643 time= 0.01563
Epoch: 0026 train_loss= 1.38310 train_acc= 0.30945 val_loss= 1.36445 val_acc= 0.46429 time= 0.01563
Epoch: 0027 train_loss= 1.38292 train_acc= 0.31270 val_loss= 1.36442 val_acc= 0.35714 time= 0.01563
Epoch: 0028 train_loss= 1.38249 train_acc= 0.29316 val_loss= 1.36452 val_acc= 0.33929 time= 0.01563
Epoch: 0029 train_loss= 1.38259 train_acc= 0.30293 val_loss= 1.36461 val_acc= 0.33929 time= 0.01563
Epoch: 0030 train_loss= 1.38234 train_acc= 0.29967 val_loss= 1.36454 val_acc= 0.33929 time= 0.01563
Epoch: 0031 train_loss= 1.38223 train_acc= 0.27362 val_loss= 1.36427 val_acc= 0.33929 time= 0.01563
Epoch: 0032 train_loss= 1.38237 train_acc= 0.28339 val_loss= 1.36400 val_acc= 0.33929 time= 0.01563
Epoch: 0033 train_loss= 1.38200 train_acc= 0.28664 val_loss= 1.36359 val_acc= 0.33929 time= 0.00000
Epoch: 0034 train_loss= 1.38237 train_acc= 0.28664 val_loss= 1.36308 val_acc= 0.33929 time= 0.01563
Epoch: 0035 train_loss= 1.38149 train_acc= 0.29316 val_loss= 1.36258 val_acc= 0.33929 time= 0.01563
Epoch: 0036 train_loss= 1.38186 train_acc= 0.29316 val_loss= 1.36228 val_acc= 0.33929 time= 0.01563
Epoch: 0037 train_loss= 1.38176 train_acc= 0.29642 val_loss= 1.36205 val_acc= 0.33929 time= 0.01563
Epoch: 0038 train_loss= 1.38170 train_acc= 0.28664 val_loss= 1.36161 val_acc= 0.33929 time= 0.01563
Epoch: 0039 train_loss= 1.38160 train_acc= 0.27036 val_loss= 1.36120 val_acc= 0.33929 time= 0.01563
Epoch: 0040 train_loss= 1.38180 train_acc= 0.27036 val_loss= 1.36075 val_acc= 0.33929 time= 0.01563
Epoch: 0041 train_loss= 1.38164 train_acc= 0.27362 val_loss= 1.36054 val_acc= 0.33929 time= 0.01563
Epoch: 0042 train_loss= 1.38121 train_acc= 0.27036 val_loss= 1.36022 val_acc= 0.33929 time= 0.01563
Epoch: 0043 train_loss= 1.38165 train_acc= 0.27687 val_loss= 1.36005 val_acc= 0.33929 time= 0.01563
Epoch: 0044 train_loss= 1.38147 train_acc= 0.28013 val_loss= 1.35994 val_acc= 0.33929 time= 0.01562
Epoch: 0045 train_loss= 1.38108 train_acc= 0.29316 val_loss= 1.35985 val_acc= 0.33929 time= 0.01563
Epoch: 0046 train_loss= 1.38119 train_acc= 0.26384 val_loss= 1.35977 val_acc= 0.33929 time= 0.01563
Epoch: 0047 train_loss= 1.38104 train_acc= 0.31270 val_loss= 1.35947 val_acc= 0.33929 time= 0.01563
Epoch: 0048 train_loss= 1.38132 train_acc= 0.28990 val_loss= 1.35938 val_acc= 0.33929 time= 0.01563
Epoch: 0049 train_loss= 1.38136 train_acc= 0.29316 val_loss= 1.35911 val_acc= 0.33929 time= 0.01563
Epoch: 0050 train_loss= 1.38114 train_acc= 0.28339 val_loss= 1.35917 val_acc= 0.33929 time= 0.01563
Epoch: 0051 train_loss= 1.38137 train_acc= 0.27687 val_loss= 1.35925 val_acc= 0.33929 time= 0.01563
Epoch: 0052 train_loss= 1.38117 train_acc= 0.27036 val_loss= 1.35951 val_acc= 0.33929 time= 0.01563
Epoch: 0053 train_loss= 1.38126 train_acc= 0.27036 val_loss= 1.35969 val_acc= 0.33929 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38905 accuracy= 0.21239 time= 0.00000 
