Epoch: 0001 train_loss= 1.92852 train_acc= 0.25838 val_loss= 1.54090 val_acc= 0.25000 time= 0.95357
Epoch: 0002 train_loss= 3.15408 train_acc= 0.26536 val_loss= 1.44640 val_acc= 0.32143 time= 0.03125
Epoch: 0003 train_loss= 1.69943 train_acc= 0.24022 val_loss= 1.42778 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.46625 train_acc= 0.23464 val_loss= 1.41601 val_acc= 0.28571 time= 0.03125
Epoch: 0005 train_loss= 1.47176 train_acc= 0.27235 val_loss= 1.41736 val_acc= 0.30357 time= 0.01563
Epoch: 0006 train_loss= 1.51217 train_acc= 0.23464 val_loss= 1.41941 val_acc= 0.28571 time= 0.03125
Epoch: 0007 train_loss= 1.42184 train_acc= 0.22626 val_loss= 1.42126 val_acc= 0.30357 time= 0.03125
Epoch: 0008 train_loss= 1.45932 train_acc= 0.25419 val_loss= 1.42138 val_acc= 0.28571 time= 0.01563
Epoch: 0009 train_loss= 1.46494 train_acc= 0.27514 val_loss= 1.41965 val_acc= 0.28571 time= 0.01562
Epoch: 0010 train_loss= 1.50790 train_acc= 0.24441 val_loss= 1.41648 val_acc= 0.28571 time= 0.03125
Epoch: 0011 train_loss= 1.48030 train_acc= 0.25978 val_loss= 1.41279 val_acc= 0.28571 time= 0.01563
Epoch: 0012 train_loss= 1.39840 train_acc= 0.26397 val_loss= 1.40906 val_acc= 0.28571 time= 0.03125
Epoch: 0013 train_loss= 1.41045 train_acc= 0.26117 val_loss= 1.40570 val_acc= 0.28571 time= 0.01563
Epoch: 0014 train_loss= 1.39409 train_acc= 0.26536 val_loss= 1.40278 val_acc= 0.28571 time= 0.03125
Epoch: 0015 train_loss= 1.40068 train_acc= 0.28631 val_loss= 1.39984 val_acc= 0.30357 time= 0.01562
Epoch: 0016 train_loss= 1.39505 train_acc= 0.29330 val_loss= 1.39740 val_acc= 0.33929 time= 0.03125
Epoch: 0017 train_loss= 1.40319 train_acc= 0.25140 val_loss= 1.39521 val_acc= 0.28571 time= 0.01563
Epoch: 0018 train_loss= 1.39708 train_acc= 0.26955 val_loss= 1.39316 val_acc= 0.26786 time= 0.03125
Epoch: 0019 train_loss= 1.39547 train_acc= 0.29190 val_loss= 1.39131 val_acc= 0.30357 time= 0.01563
Epoch: 0020 train_loss= 1.39831 train_acc= 0.26117 val_loss= 1.38937 val_acc= 0.28571 time= 0.03125
Epoch: 0021 train_loss= 1.38457 train_acc= 0.29888 val_loss= 1.38762 val_acc= 0.26786 time= 0.01563
Epoch: 0022 train_loss= 1.39593 train_acc= 0.27235 val_loss= 1.38600 val_acc= 0.26786 time= 0.03125
Epoch: 0023 train_loss= 1.38980 train_acc= 0.31145 val_loss= 1.38456 val_acc= 0.30357 time= 0.01563
Epoch: 0024 train_loss= 1.38770 train_acc= 0.29190 val_loss= 1.38328 val_acc= 0.30357 time= 0.03125
Epoch: 0025 train_loss= 1.39189 train_acc= 0.28492 val_loss= 1.38207 val_acc= 0.30357 time= 0.01563
Epoch: 0026 train_loss= 1.38791 train_acc= 0.29609 val_loss= 1.38088 val_acc= 0.32143 time= 0.03125
Epoch: 0027 train_loss= 1.39171 train_acc= 0.28352 val_loss= 1.37998 val_acc= 0.30357 time= 0.03125
Epoch: 0028 train_loss= 1.38298 train_acc= 0.30726 val_loss= 1.37908 val_acc= 0.30357 time= 0.03125
Epoch: 0029 train_loss= 1.38812 train_acc= 0.28212 val_loss= 1.37821 val_acc= 0.30357 time= 0.01563
Epoch: 0030 train_loss= 1.40625 train_acc= 0.27793 val_loss= 1.37757 val_acc= 0.30357 time= 0.03125
Epoch: 0031 train_loss= 1.38850 train_acc= 0.31285 val_loss= 1.37690 val_acc= 0.30357 time= 0.01563
Epoch: 0032 train_loss= 1.39078 train_acc= 0.28771 val_loss= 1.37626 val_acc= 0.30357 time= 0.03125
Epoch: 0033 train_loss= 1.37975 train_acc= 0.31704 val_loss= 1.37566 val_acc= 0.30357 time= 0.01562
Epoch: 0034 train_loss= 1.38205 train_acc= 0.30307 val_loss= 1.37505 val_acc= 0.30357 time= 0.01562
Epoch: 0035 train_loss= 1.39176 train_acc= 0.28911 val_loss= 1.37469 val_acc= 0.30357 time= 0.03125
Epoch: 0036 train_loss= 1.38494 train_acc= 0.30168 val_loss= 1.37437 val_acc= 0.28571 time= 0.01563
Epoch: 0037 train_loss= 1.38826 train_acc= 0.28771 val_loss= 1.37402 val_acc= 0.30357 time= 0.03125
Epoch: 0038 train_loss= 1.38324 train_acc= 0.28352 val_loss= 1.37385 val_acc= 0.30357 time= 0.01563
Epoch: 0039 train_loss= 1.39004 train_acc= 0.29050 val_loss= 1.37375 val_acc= 0.32143 time= 0.03125
Epoch: 0040 train_loss= 1.38799 train_acc= 0.31145 val_loss= 1.37386 val_acc= 0.30357 time= 0.01563
Epoch: 0041 train_loss= 1.38590 train_acc= 0.30028 val_loss= 1.37400 val_acc= 0.30357 time= 0.03125
Epoch: 0042 train_loss= 1.38363 train_acc= 0.29469 val_loss= 1.37392 val_acc= 0.30357 time= 0.01563
Epoch: 0043 train_loss= 1.37849 train_acc= 0.30168 val_loss= 1.37371 val_acc= 0.33929 time= 0.01563
Epoch: 0044 train_loss= 1.37952 train_acc= 0.30447 val_loss= 1.37337 val_acc= 0.33929 time= 0.03125
Epoch: 0045 train_loss= 1.37641 train_acc= 0.31564 val_loss= 1.37289 val_acc= 0.37500 time= 0.01563
Epoch: 0046 train_loss= 1.38376 train_acc= 0.29888 val_loss= 1.37245 val_acc= 0.35714 time= 0.03125
Epoch: 0047 train_loss= 1.38023 train_acc= 0.28492 val_loss= 1.37196 val_acc= 0.35714 time= 0.01563
Epoch: 0048 train_loss= 1.38123 train_acc= 0.31285 val_loss= 1.37152 val_acc= 0.35714 time= 0.03125
Epoch: 0049 train_loss= 1.39061 train_acc= 0.29469 val_loss= 1.37106 val_acc= 0.33929 time= 0.01563
Epoch: 0050 train_loss= 1.38227 train_acc= 0.29749 val_loss= 1.37083 val_acc= 0.33929 time= 0.01562
Epoch: 0051 train_loss= 1.38128 train_acc= 0.27793 val_loss= 1.37065 val_acc= 0.33929 time= 0.03125
Epoch: 0052 train_loss= 1.38456 train_acc= 0.30307 val_loss= 1.37037 val_acc= 0.33929 time= 0.01563
Epoch: 0053 train_loss= 1.37487 train_acc= 0.29749 val_loss= 1.36982 val_acc= 0.33929 time= 0.03125
Epoch: 0054 train_loss= 1.37921 train_acc= 0.31006 val_loss= 1.36929 val_acc= 0.32143 time= 0.01563
Epoch: 0055 train_loss= 1.37913 train_acc= 0.31006 val_loss= 1.36874 val_acc= 0.28571 time= 0.01563
Epoch: 0056 train_loss= 1.37727 train_acc= 0.29609 val_loss= 1.36812 val_acc= 0.28571 time= 0.03125
Epoch: 0057 train_loss= 1.38133 train_acc= 0.30587 val_loss= 1.36763 val_acc= 0.28571 time= 0.01563
Epoch: 0058 train_loss= 1.37745 train_acc= 0.28911 val_loss= 1.36715 val_acc= 0.26786 time= 0.03125
Epoch: 0059 train_loss= 1.38252 train_acc= 0.28631 val_loss= 1.36660 val_acc= 0.26786 time= 0.01563
Epoch: 0060 train_loss= 1.37791 train_acc= 0.31285 val_loss= 1.36604 val_acc= 0.26786 time= 0.03125
Epoch: 0061 train_loss= 1.38567 train_acc= 0.28212 val_loss= 1.36525 val_acc= 0.23214 time= 0.01563
Epoch: 0062 train_loss= 1.37340 train_acc= 0.31844 val_loss= 1.36444 val_acc= 0.25000 time= 0.03125
Epoch: 0063 train_loss= 1.37274 train_acc= 0.31285 val_loss= 1.36375 val_acc= 0.25000 time= 0.01563
Epoch: 0064 train_loss= 1.37956 train_acc= 0.30447 val_loss= 1.36316 val_acc= 0.25000 time= 0.01563
Epoch: 0065 train_loss= 1.38015 train_acc= 0.31145 val_loss= 1.36337 val_acc= 0.25000 time= 0.03125
Epoch: 0066 train_loss= 1.37797 train_acc= 0.31704 val_loss= 1.36405 val_acc= 0.25000 time= 0.01563
Epoch: 0067 train_loss= 1.37645 train_acc= 0.30028 val_loss= 1.36468 val_acc= 0.25000 time= 0.03125
Epoch: 0068 train_loss= 1.38057 train_acc= 0.28212 val_loss= 1.36504 val_acc= 0.28571 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.38461 accuracy= 0.30088 time= 0.01563 
