Epoch: 0001 train_loss= 0.70103 train_acc= 0.51299 val_loss= 0.69789 val_acc= 0.52459 time= 0.35941
Epoch: 0002 train_loss= 0.69808 train_acc= 0.51558 val_loss= 0.69553 val_acc= 0.52459 time= 0.00000
Epoch: 0003 train_loss= 0.69590 train_acc= 0.51299 val_loss= 0.69386 val_acc= 0.52459 time= 0.01562
Epoch: 0004 train_loss= 0.69443 train_acc= 0.51039 val_loss= 0.69280 val_acc= 0.52459 time= 0.01563
Epoch: 0005 train_loss= 0.69359 train_acc= 0.51169 val_loss= 0.69221 val_acc= 0.52459 time= 0.00000
Epoch: 0006 train_loss= 0.69311 train_acc= 0.51039 val_loss= 0.69195 val_acc= 0.52459 time= 0.01563
Epoch: 0007 train_loss= 0.69290 train_acc= 0.51429 val_loss= 0.69192 val_acc= 0.52459 time= 0.01563
Epoch: 0008 train_loss= 0.69299 train_acc= 0.51429 val_loss= 0.69201 val_acc= 0.52459 time= 0.00000
Epoch: 0009 train_loss= 0.69293 train_acc= 0.51299 val_loss= 0.69212 val_acc= 0.52459 time= 0.01563
Epoch: 0010 train_loss= 0.69300 train_acc= 0.52078 val_loss= 0.69217 val_acc= 0.52459 time= 0.01563
Epoch: 0011 train_loss= 0.69305 train_acc= 0.51948 val_loss= 0.69215 val_acc= 0.52459 time= 0.01563
Epoch: 0012 train_loss= 0.69311 train_acc= 0.51818 val_loss= 0.69202 val_acc= 0.52459 time= 0.00000
Epoch: 0013 train_loss= 0.69275 train_acc= 0.51948 val_loss= 0.69184 val_acc= 0.52459 time= 0.01563
Epoch: 0014 train_loss= 0.69250 train_acc= 0.53117 val_loss= 0.69159 val_acc= 0.52459 time= 0.01563
Epoch: 0015 train_loss= 0.69242 train_acc= 0.52338 val_loss= 0.69133 val_acc= 0.52459 time= 0.00000
Epoch: 0016 train_loss= 0.69222 train_acc= 0.52338 val_loss= 0.69109 val_acc= 0.52459 time= 0.01563
Epoch: 0017 train_loss= 0.69233 train_acc= 0.52727 val_loss= 0.69092 val_acc= 0.52459 time= 0.01563
Epoch: 0018 train_loss= 0.69174 train_acc= 0.52597 val_loss= 0.69086 val_acc= 0.54098 time= 0.00000
Epoch: 0019 train_loss= 0.69145 train_acc= 0.54286 val_loss= 0.69082 val_acc= 0.55738 time= 0.01563
Epoch: 0020 train_loss= 0.69109 train_acc= 0.54026 val_loss= 0.69076 val_acc= 0.55738 time= 0.01563
Epoch: 0021 train_loss= 0.69170 train_acc= 0.54156 val_loss= 0.69055 val_acc= 0.55738 time= 0.00000
Epoch: 0022 train_loss= 0.69105 train_acc= 0.54935 val_loss= 0.69025 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.69136 train_acc= 0.54416 val_loss= 0.69007 val_acc= 0.55738 time= 0.01563
Epoch: 0024 train_loss= 0.69125 train_acc= 0.54545 val_loss= 0.68982 val_acc= 0.54098 time= 0.00000
Epoch: 0025 train_loss= 0.69117 train_acc= 0.54156 val_loss= 0.68962 val_acc= 0.54098 time= 0.01563
Epoch: 0026 train_loss= 0.69066 train_acc= 0.53117 val_loss= 0.68965 val_acc= 0.55738 time= 0.01562
Epoch: 0027 train_loss= 0.69051 train_acc= 0.54545 val_loss= 0.68980 val_acc= 0.55738 time= 0.00000
Epoch: 0028 train_loss= 0.69032 train_acc= 0.54286 val_loss= 0.69008 val_acc= 0.57377 time= 0.01563
Epoch: 0029 train_loss= 0.69033 train_acc= 0.58701 val_loss= 0.69006 val_acc= 0.57377 time= 0.01563
Epoch: 0030 train_loss= 0.69079 train_acc= 0.58701 val_loss= 0.68962 val_acc= 0.57377 time= 0.01563
Epoch: 0031 train_loss= 0.68994 train_acc= 0.56623 val_loss= 0.68900 val_acc= 0.55738 time= 0.00000
Epoch: 0032 train_loss= 0.68939 train_acc= 0.54416 val_loss= 0.68875 val_acc= 0.55738 time= 0.01563
Epoch: 0033 train_loss= 0.68979 train_acc= 0.54935 val_loss= 0.68908 val_acc= 0.57377 time= 0.01563
Epoch: 0034 train_loss= 0.68919 train_acc= 0.54416 val_loss= 0.68981 val_acc= 0.62295 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 0.69307 accuracy= 0.51639 time= 0.01563 
