Epoch: 0001 train_loss= 1.53319 train_acc= 0.52424 val_loss= 1.41628 val_acc= 0.49180 time= 0.31252
Epoch: 0002 train_loss= 2.09628 train_acc= 0.55758 val_loss= 1.34297 val_acc= 0.50820 time= 0.03125
Epoch: 0003 train_loss= 0.88439 train_acc= 0.48182 val_loss= 1.27654 val_acc= 0.49180 time= 0.01563
Epoch: 0004 train_loss= 2.35135 train_acc= 0.48485 val_loss= 0.81273 val_acc= 0.54098 time= 0.03125
Epoch: 0005 train_loss= 1.06821 train_acc= 0.47273 val_loss= 0.87916 val_acc= 0.52459 time= 0.01563
Epoch: 0006 train_loss= 0.83356 train_acc= 0.55455 val_loss= 1.32377 val_acc= 0.54098 time= 0.03125
Epoch: 0007 train_loss= 0.80768 train_acc= 0.50000 val_loss= 1.61576 val_acc= 0.54098 time= 0.03125
Epoch: 0008 train_loss= 1.15373 train_acc= 0.54848 val_loss= 1.65342 val_acc= 0.52459 time= 0.01563
Epoch: 0009 train_loss= 1.34350 train_acc= 0.55152 val_loss= 1.60314 val_acc= 0.52459 time= 0.01563
Epoch: 0010 train_loss= 0.95849 train_acc= 0.56970 val_loss= 1.49369 val_acc= 0.52459 time= 0.03125
Epoch: 0011 train_loss= 0.87097 train_acc= 0.55455 val_loss= 1.35377 val_acc= 0.55738 time= 0.01563
Epoch: 0012 train_loss= 1.05673 train_acc= 0.50606 val_loss= 1.20324 val_acc= 0.55738 time= 0.03125
Epoch: 0013 train_loss= 1.08508 train_acc= 0.52121 val_loss= 1.01210 val_acc= 0.54098 time= 0.02054
Epoch: 0014 train_loss= 0.84556 train_acc= 0.54242 val_loss= 0.81224 val_acc= 0.52459 time= 0.02663
Epoch: 0015 train_loss= 1.44636 train_acc= 0.50606 val_loss= 0.72151 val_acc= 0.52459 time= 0.01563
Epoch: 0016 train_loss= 0.81572 train_acc= 0.53636 val_loss= 0.69731 val_acc= 0.47541 time= 0.01563
Epoch: 0017 train_loss= 0.83919 train_acc= 0.54242 val_loss= 0.71209 val_acc= 0.52459 time= 0.03125
Epoch: 0018 train_loss= 1.35336 train_acc= 0.53939 val_loss= 0.70887 val_acc= 0.49180 time= 0.01563
Epoch: 0019 train_loss= 1.05040 train_acc= 0.54545 val_loss= 0.69871 val_acc= 0.45902 time= 0.03125
Epoch: 0020 train_loss= 0.73433 train_acc= 0.51515 val_loss= 0.69377 val_acc= 0.50820 time= 0.01562
Epoch: 0021 train_loss= 0.76754 train_acc= 0.51212 val_loss= 0.69284 val_acc= 0.50820 time= 0.01563
Epoch: 0022 train_loss= 0.79533 train_acc= 0.52424 val_loss= 0.69273 val_acc= 0.47541 time= 0.01563
Epoch: 0023 train_loss= 0.81972 train_acc= 0.53333 val_loss= 0.69275 val_acc= 0.47541 time= 0.01563
Epoch: 0024 train_loss= 0.70710 train_acc= 0.53636 val_loss= 0.69287 val_acc= 0.49180 time= 0.03125
Epoch: 0025 train_loss= 0.70658 train_acc= 0.52121 val_loss= 0.69309 val_acc= 0.49180 time= 0.01563
Epoch: 0026 train_loss= 0.70940 train_acc= 0.52727 val_loss= 0.69322 val_acc= 0.49180 time= 0.01563
Epoch: 0027 train_loss= 0.78979 train_acc= 0.53030 val_loss= 0.69313 val_acc= 0.45902 time= 0.03125
Epoch: 0028 train_loss= 0.70658 train_acc= 0.47576 val_loss= 0.69273 val_acc= 0.45902 time= 0.01563
Epoch: 0029 train_loss= 0.70230 train_acc= 0.57273 val_loss= 0.69231 val_acc= 0.44262 time= 0.03125
Epoch: 0030 train_loss= 0.69081 train_acc= 0.58182 val_loss= 0.69196 val_acc= 0.45902 time= 0.03125
Epoch: 0031 train_loss= 0.70158 train_acc= 0.53636 val_loss= 0.69171 val_acc= 0.45902 time= 0.01563
Epoch: 0032 train_loss= 0.70862 train_acc= 0.50000 val_loss= 0.69148 val_acc= 0.50820 time= 0.03125
Epoch: 0033 train_loss= 0.69535 train_acc= 0.54848 val_loss= 0.69128 val_acc= 0.50820 time= 0.01563
Epoch: 0034 train_loss= 0.70757 train_acc= 0.53030 val_loss= 0.69108 val_acc= 0.52459 time= 0.03125
Epoch: 0035 train_loss= 0.70079 train_acc= 0.49697 val_loss= 0.69095 val_acc= 0.52459 time= 0.01563
Epoch: 0036 train_loss= 0.68823 train_acc= 0.56970 val_loss= 0.69081 val_acc= 0.54098 time= 0.03249
Epoch: 0037 train_loss= 0.69557 train_acc= 0.53636 val_loss= 0.69068 val_acc= 0.54098 time= 0.03125
Epoch: 0038 train_loss= 0.70125 train_acc= 0.52424 val_loss= 0.69057 val_acc= 0.55738 time= 0.03125
Epoch: 0039 train_loss= 0.69974 train_acc= 0.55152 val_loss= 0.69049 val_acc= 0.55738 time= 0.01562
Epoch: 0040 train_loss= 0.70584 train_acc= 0.52121 val_loss= 0.69045 val_acc= 0.55738 time= 0.03125
Epoch: 0041 train_loss= 0.70096 train_acc= 0.51515 val_loss= 0.69043 val_acc= 0.55738 time= 0.01563
Epoch: 0042 train_loss= 0.68402 train_acc= 0.57879 val_loss= 0.69039 val_acc= 0.55738 time= 0.03125
Epoch: 0043 train_loss= 0.67735 train_acc= 0.54545 val_loss= 0.69035 val_acc= 0.55738 time= 0.01563
Epoch: 0044 train_loss= 0.70076 train_acc= 0.56364 val_loss= 0.69034 val_acc= 0.55738 time= 0.03125
Epoch: 0045 train_loss= 0.69301 train_acc= 0.55758 val_loss= 0.69035 val_acc= 0.55738 time= 0.01563
Epoch: 0046 train_loss= 0.69518 train_acc= 0.52424 val_loss= 0.69041 val_acc= 0.57377 time= 0.03125
Epoch: 0047 train_loss= 0.68867 train_acc= 0.51212 val_loss= 0.69045 val_acc= 0.57377 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.70410 accuracy= 0.47541 time= 0.01563 
