Epoch: 0001 train_loss= 0.97851 train_acc= 0.49697 val_loss= 0.78720 val_acc= 0.47541 time= 0.07855
Epoch: 0002 train_loss= 1.16175 train_acc= 0.51212 val_loss= 0.86620 val_acc= 0.55738 time= 0.01521
Epoch: 0003 train_loss= 0.81960 train_acc= 0.52121 val_loss= 1.35604 val_acc= 0.55738 time= 0.01563
Epoch: 0004 train_loss= 1.27898 train_acc= 0.54545 val_loss= 1.37330 val_acc= 0.55738 time= 0.00000
Epoch: 0005 train_loss= 1.01166 train_acc= 0.50909 val_loss= 1.29400 val_acc= 0.55738 time= 0.01563
Epoch: 0006 train_loss= 1.00432 train_acc= 0.54545 val_loss= 1.19724 val_acc= 0.55738 time= 0.01563
Epoch: 0007 train_loss= 1.88393 train_acc= 0.51212 val_loss= 1.05309 val_acc= 0.55738 time= 0.01563
Epoch: 0008 train_loss= 1.30013 train_acc= 0.50000 val_loss= 0.87898 val_acc= 0.55738 time= 0.00000
Epoch: 0009 train_loss= 1.97753 train_acc= 0.49697 val_loss= 0.73271 val_acc= 0.57377 time= 0.01563
Epoch: 0010 train_loss= 0.88937 train_acc= 0.54848 val_loss= 0.72606 val_acc= 0.45902 time= 0.01563
Epoch: 0011 train_loss= 0.79848 train_acc= 0.52727 val_loss= 0.85638 val_acc= 0.44262 time= 0.01563
Epoch: 0012 train_loss= 1.47332 train_acc= 0.49091 val_loss= 0.92423 val_acc= 0.42623 time= 0.01563
Epoch: 0013 train_loss= 0.94412 train_acc= 0.46667 val_loss= 0.93908 val_acc= 0.42623 time= 0.00000
Epoch: 0014 train_loss= 0.94082 train_acc= 0.44242 val_loss= 0.95011 val_acc= 0.42623 time= 0.01563
Epoch: 0015 train_loss= 0.95551 train_acc= 0.47879 val_loss= 0.95223 val_acc= 0.44262 time= 0.01563
Epoch: 0016 train_loss= 1.19928 train_acc= 0.46970 val_loss= 0.88573 val_acc= 0.44262 time= 0.01563
Epoch: 0017 train_loss= 1.50064 train_acc= 0.47879 val_loss= 0.78901 val_acc= 0.44262 time= 0.01563
Epoch: 0018 train_loss= 1.30502 train_acc= 0.51515 val_loss= 0.71989 val_acc= 0.42623 time= 0.00000
Epoch: 0019 train_loss= 0.78965 train_acc= 0.49394 val_loss= 0.70793 val_acc= 0.54098 time= 0.01563
Epoch: 0020 train_loss= 0.84330 train_acc= 0.46061 val_loss= 0.72564 val_acc= 0.57377 time= 0.01563
Epoch: 0021 train_loss= 0.87621 train_acc= 0.49091 val_loss= 0.74847 val_acc= 0.55738 time= 0.01563
Epoch: 0022 train_loss= 0.77159 train_acc= 0.53636 val_loss= 0.77009 val_acc= 0.55738 time= 0.01563
Epoch: 0023 train_loss= 0.73147 train_acc= 0.50000 val_loss= 0.78750 val_acc= 0.54098 time= 0.00000
Epoch: 0024 train_loss= 0.87537 train_acc= 0.52121 val_loss= 0.79307 val_acc= 0.55738 time= 0.00000
Epoch: 0025 train_loss= 0.96708 train_acc= 0.48485 val_loss= 0.79083 val_acc= 0.55738 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 0.74931 accuracy= 0.48361 time= 0.01563 
