Epoch: 0001 train_loss= 1.39401 train_acc= 0.27235 val_loss= 1.39093 val_acc= 0.30357 time= 0.78130
Epoch: 0002 train_loss= 1.39085 train_acc= 0.30168 val_loss= 1.38819 val_acc= 0.30357 time= 0.00000
Epoch: 0003 train_loss= 1.38816 train_acc= 0.30726 val_loss= 1.38600 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.38608 train_acc= 0.30726 val_loss= 1.38435 val_acc= 0.30357 time= 0.01563
Epoch: 0005 train_loss= 1.38436 train_acc= 0.30587 val_loss= 1.38316 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.38328 train_acc= 0.30447 val_loss= 1.38235 val_acc= 0.30357 time= 0.02028
Epoch: 0007 train_loss= 1.38235 train_acc= 0.30587 val_loss= 1.38179 val_acc= 0.30357 time= 0.01100
Epoch: 0008 train_loss= 1.38187 train_acc= 0.30587 val_loss= 1.38144 val_acc= 0.30357 time= 0.01563
Epoch: 0009 train_loss= 1.38135 train_acc= 0.30587 val_loss= 1.38121 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.38159 train_acc= 0.30587 val_loss= 1.38101 val_acc= 0.30357 time= 0.01563
Epoch: 0011 train_loss= 1.38157 train_acc= 0.30587 val_loss= 1.38079 val_acc= 0.30357 time= 0.00000
Epoch: 0012 train_loss= 1.38031 train_acc= 0.30587 val_loss= 1.38057 val_acc= 0.30357 time= 0.01563
Epoch: 0013 train_loss= 1.38060 train_acc= 0.30587 val_loss= 1.38035 val_acc= 0.30357 time= 0.01563
Epoch: 0014 train_loss= 1.38032 train_acc= 0.30587 val_loss= 1.38010 val_acc= 0.30357 time= 0.01563
Epoch: 0015 train_loss= 1.37996 train_acc= 0.30587 val_loss= 1.37982 val_acc= 0.30357 time= 0.01563
Epoch: 0016 train_loss= 1.37963 train_acc= 0.30587 val_loss= 1.37953 val_acc= 0.30357 time= 0.01563
Epoch: 0017 train_loss= 1.37929 train_acc= 0.30587 val_loss= 1.37924 val_acc= 0.30357 time= 0.01563
Epoch: 0018 train_loss= 1.37878 train_acc= 0.30587 val_loss= 1.37898 val_acc= 0.30357 time= 0.01563
Epoch: 0019 train_loss= 1.37852 train_acc= 0.30587 val_loss= 1.37875 val_acc= 0.30357 time= 0.00000
Epoch: 0020 train_loss= 1.37839 train_acc= 0.30587 val_loss= 1.37852 val_acc= 0.30357 time= 0.01563
Epoch: 0021 train_loss= 1.37725 train_acc= 0.30587 val_loss= 1.37827 val_acc= 0.30357 time= 0.01563
Epoch: 0022 train_loss= 1.37766 train_acc= 0.30587 val_loss= 1.37804 val_acc= 0.30357 time= 0.01563
Epoch: 0023 train_loss= 1.37757 train_acc= 0.30587 val_loss= 1.37778 val_acc= 0.30357 time= 0.01563
Epoch: 0024 train_loss= 1.37694 train_acc= 0.30587 val_loss= 1.37750 val_acc= 0.30357 time= 0.01563
Epoch: 0025 train_loss= 1.37724 train_acc= 0.30587 val_loss= 1.37727 val_acc= 0.30357 time= 0.01562
Epoch: 0026 train_loss= 1.37645 train_acc= 0.30587 val_loss= 1.37708 val_acc= 0.30357 time= 0.01563
Epoch: 0027 train_loss= 1.37622 train_acc= 0.30587 val_loss= 1.37690 val_acc= 0.30357 time= 0.00000
Epoch: 0028 train_loss= 1.37587 train_acc= 0.30587 val_loss= 1.37673 val_acc= 0.30357 time= 0.01563
Epoch: 0029 train_loss= 1.37592 train_acc= 0.30587 val_loss= 1.37660 val_acc= 0.30357 time= 0.01562
Epoch: 0030 train_loss= 1.37624 train_acc= 0.30587 val_loss= 1.37649 val_acc= 0.30357 time= 0.01563
Epoch: 0031 train_loss= 1.37636 train_acc= 0.30587 val_loss= 1.37641 val_acc= 0.30357 time= 0.01563
Epoch: 0032 train_loss= 1.37601 train_acc= 0.30587 val_loss= 1.37628 val_acc= 0.30357 time= 0.00000
Epoch: 0033 train_loss= 1.37549 train_acc= 0.30587 val_loss= 1.37614 val_acc= 0.30357 time= 0.01563
Epoch: 0034 train_loss= 1.37554 train_acc= 0.30587 val_loss= 1.37596 val_acc= 0.30357 time= 0.01563
Epoch: 0035 train_loss= 1.37510 train_acc= 0.30587 val_loss= 1.37577 val_acc= 0.30357 time= 0.01563
Epoch: 0036 train_loss= 1.37507 train_acc= 0.30587 val_loss= 1.37557 val_acc= 0.30357 time= 0.00000
Epoch: 0037 train_loss= 1.37510 train_acc= 0.30587 val_loss= 1.37538 val_acc= 0.30357 time= 0.01563
Epoch: 0038 train_loss= 1.37471 train_acc= 0.30587 val_loss= 1.37522 val_acc= 0.30357 time= 0.01563
Epoch: 0039 train_loss= 1.37527 train_acc= 0.30587 val_loss= 1.37510 val_acc= 0.30357 time= 0.01563
Epoch: 0040 train_loss= 1.37447 train_acc= 0.30726 val_loss= 1.37501 val_acc= 0.30357 time= 0.01563
Epoch: 0041 train_loss= 1.37438 train_acc= 0.30726 val_loss= 1.37493 val_acc= 0.30357 time= 0.01563
Epoch: 0042 train_loss= 1.37403 train_acc= 0.30587 val_loss= 1.37482 val_acc= 0.30357 time= 0.01563
Epoch: 0043 train_loss= 1.37416 train_acc= 0.30587 val_loss= 1.37471 val_acc= 0.30357 time= 0.01563
Epoch: 0044 train_loss= 1.37451 train_acc= 0.30587 val_loss= 1.37465 val_acc= 0.30357 time= 0.01563
Epoch: 0045 train_loss= 1.37394 train_acc= 0.30587 val_loss= 1.37457 val_acc= 0.30357 time= 0.01563
Epoch: 0046 train_loss= 1.37361 train_acc= 0.30587 val_loss= 1.37446 val_acc= 0.30357 time= 0.01562
Epoch: 0047 train_loss= 1.37346 train_acc= 0.30587 val_loss= 1.37426 val_acc= 0.30357 time= 0.01563
Epoch: 0048 train_loss= 1.37441 train_acc= 0.30587 val_loss= 1.37404 val_acc= 0.30357 time= 0.01563
Epoch: 0049 train_loss= 1.37335 train_acc= 0.30587 val_loss= 1.37389 val_acc= 0.30357 time= 0.01563
Epoch: 0050 train_loss= 1.37359 train_acc= 0.30587 val_loss= 1.37377 val_acc= 0.30357 time= 0.01563
Epoch: 0051 train_loss= 1.37294 train_acc= 0.30587 val_loss= 1.37368 val_acc= 0.30357 time= 0.00000
Epoch: 0052 train_loss= 1.37354 train_acc= 0.30587 val_loss= 1.37364 val_acc= 0.30357 time= 0.01563
Epoch: 0053 train_loss= 1.37280 train_acc= 0.30587 val_loss= 1.37352 val_acc= 0.30357 time= 0.01563
Epoch: 0054 train_loss= 1.37289 train_acc= 0.30587 val_loss= 1.37337 val_acc= 0.30357 time= 0.01563
Epoch: 0055 train_loss= 1.37231 train_acc= 0.30866 val_loss= 1.37324 val_acc= 0.30357 time= 0.01563
Epoch: 0056 train_loss= 1.37210 train_acc= 0.30726 val_loss= 1.37319 val_acc= 0.30357 time= 0.01563
Epoch: 0057 train_loss= 1.37281 train_acc= 0.30587 val_loss= 1.37308 val_acc= 0.30357 time= 0.01563
Epoch: 0058 train_loss= 1.37224 train_acc= 0.30587 val_loss= 1.37297 val_acc= 0.30357 time= 0.00000
Epoch: 0059 train_loss= 1.37253 train_acc= 0.30587 val_loss= 1.37299 val_acc= 0.30357 time= 0.00000
Epoch: 0060 train_loss= 1.37264 train_acc= 0.30587 val_loss= 1.37311 val_acc= 0.30357 time= 0.01563
Epoch: 0061 train_loss= 1.37228 train_acc= 0.30726 val_loss= 1.37310 val_acc= 0.30357 time= 0.01562
Epoch: 0062 train_loss= 1.37250 train_acc= 0.30726 val_loss= 1.37295 val_acc= 0.30357 time= 0.01563
Epoch: 0063 train_loss= 1.37172 train_acc= 0.30726 val_loss= 1.37259 val_acc= 0.30357 time= 0.01563
Epoch: 0064 train_loss= 1.37251 train_acc= 0.30726 val_loss= 1.37219 val_acc= 0.30357 time= 0.00000
Epoch: 0065 train_loss= 1.37152 train_acc= 0.30587 val_loss= 1.37186 val_acc= 0.30357 time= 0.01563
Epoch: 0066 train_loss= 1.37148 train_acc= 0.30726 val_loss= 1.37173 val_acc= 0.30357 time= 0.01563
Epoch: 0067 train_loss= 1.37188 train_acc= 0.30726 val_loss= 1.37171 val_acc= 0.30357 time= 0.01563
Epoch: 0068 train_loss= 1.37163 train_acc= 0.30587 val_loss= 1.37174 val_acc= 0.30357 time= 0.01563
Epoch: 0069 train_loss= 1.37129 train_acc= 0.30726 val_loss= 1.37182 val_acc= 0.30357 time= 0.01563
Epoch: 0070 train_loss= 1.37162 train_acc= 0.30726 val_loss= 1.37185 val_acc= 0.30357 time= 0.00000
Epoch: 0071 train_loss= 1.37094 train_acc= 0.30726 val_loss= 1.37183 val_acc= 0.30357 time= 0.01563
Epoch: 0072 train_loss= 1.37029 train_acc= 0.30726 val_loss= 1.37180 val_acc= 0.30357 time= 0.01563
Epoch: 0073 train_loss= 1.36925 train_acc= 0.30726 val_loss= 1.37159 val_acc= 0.30357 time= 0.01563
Epoch: 0074 train_loss= 1.37066 train_acc= 0.30866 val_loss= 1.37124 val_acc= 0.30357 time= 0.01563
Epoch: 0075 train_loss= 1.37033 train_acc= 0.30726 val_loss= 1.37083 val_acc= 0.30357 time= 0.01562
Epoch: 0076 train_loss= 1.37018 train_acc= 0.30866 val_loss= 1.37067 val_acc= 0.30357 time= 0.01563
Epoch: 0077 train_loss= 1.36991 train_acc= 0.30726 val_loss= 1.37050 val_acc= 0.30357 time= 0.01563
Epoch: 0078 train_loss= 1.37048 train_acc= 0.30726 val_loss= 1.37031 val_acc= 0.30357 time= 0.00000
Epoch: 0079 train_loss= 1.37012 train_acc= 0.30726 val_loss= 1.37030 val_acc= 0.30357 time= 0.01563
Epoch: 0080 train_loss= 1.37006 train_acc= 0.30866 val_loss= 1.37047 val_acc= 0.30357 time= 0.01562
Epoch: 0081 train_loss= 1.36949 train_acc= 0.30726 val_loss= 1.37094 val_acc= 0.30357 time= 0.01563
Epoch: 0082 train_loss= 1.36970 train_acc= 0.31006 val_loss= 1.37092 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37509 accuracy= 0.30973 time= 0.00000 
