Epoch: 0001 train_loss= 0.70117 train_acc= 0.46000 val_loss= 0.69642 val_acc= 0.59016 time= 0.25002
Epoch: 0002 train_loss= 0.69788 train_acc= 0.53455 val_loss= 0.69306 val_acc= 0.59016 time= 0.01563
Epoch: 0003 train_loss= 0.69555 train_acc= 0.53455 val_loss= 0.69037 val_acc= 0.59016 time= 0.00000
Epoch: 0004 train_loss= 0.69389 train_acc= 0.53636 val_loss= 0.68828 val_acc= 0.59016 time= 0.01563
Epoch: 0005 train_loss= 0.69298 train_acc= 0.53455 val_loss= 0.68672 val_acc= 0.59016 time= 0.01563
Epoch: 0006 train_loss= 0.69250 train_acc= 0.53455 val_loss= 0.68564 val_acc= 0.59016 time= 0.01563
Epoch: 0007 train_loss= 0.69244 train_acc= 0.53455 val_loss= 0.68496 val_acc= 0.59016 time= 0.00000
Epoch: 0008 train_loss= 0.69210 train_acc= 0.53455 val_loss= 0.68448 val_acc= 0.59016 time= 0.01563
Epoch: 0009 train_loss= 0.69216 train_acc= 0.53455 val_loss= 0.68419 val_acc= 0.59016 time= 0.01563
Epoch: 0010 train_loss= 0.69194 train_acc= 0.53636 val_loss= 0.68407 val_acc= 0.59016 time= 0.00000
Epoch: 0011 train_loss= 0.69195 train_acc= 0.53455 val_loss= 0.68406 val_acc= 0.59016 time= 0.01563
Epoch: 0012 train_loss= 0.69184 train_acc= 0.53455 val_loss= 0.68411 val_acc= 0.59016 time= 0.01563
Epoch: 0013 train_loss= 0.69156 train_acc= 0.53455 val_loss= 0.68406 val_acc= 0.59016 time= 0.00000
Epoch: 0014 train_loss= 0.69202 train_acc= 0.53455 val_loss= 0.68398 val_acc= 0.59016 time= 0.01563
Epoch: 0015 train_loss= 0.69133 train_acc= 0.53636 val_loss= 0.68409 val_acc= 0.59016 time= 0.01563
Epoch: 0016 train_loss= 0.69135 train_acc= 0.54182 val_loss= 0.68401 val_acc= 0.59016 time= 0.00000
Epoch: 0017 train_loss= 0.69100 train_acc= 0.54000 val_loss= 0.68395 val_acc= 0.59016 time= 0.01563
Epoch: 0018 train_loss= 0.69058 train_acc= 0.53818 val_loss= 0.68375 val_acc= 0.59016 time= 0.01563
Epoch: 0019 train_loss= 0.69021 train_acc= 0.53818 val_loss= 0.68345 val_acc= 0.59016 time= 0.01563
Epoch: 0020 train_loss= 0.69002 train_acc= 0.54182 val_loss= 0.68316 val_acc= 0.59016 time= 0.00000
Epoch: 0021 train_loss= 0.69002 train_acc= 0.54545 val_loss= 0.68275 val_acc= 0.59016 time= 0.01563
Epoch: 0022 train_loss= 0.68994 train_acc= 0.54364 val_loss= 0.68267 val_acc= 0.59016 time= 0.01563
Epoch: 0023 train_loss= 0.68916 train_acc= 0.54182 val_loss= 0.68281 val_acc= 0.59016 time= 0.00000
Epoch: 0024 train_loss= 0.68945 train_acc= 0.54182 val_loss= 0.68310 val_acc= 0.59016 time= 0.01563
Epoch: 0025 train_loss= 0.68903 train_acc= 0.55091 val_loss= 0.68339 val_acc= 0.59016 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.69021 accuracy= 0.55738 time= 0.00000 
