Epoch: 0001 train_loss= 2.89192 train_acc= 0.25081 val_loss= 1.79644 val_acc= 0.33333 time= 0.36208
Epoch: 0002 train_loss= 1.92391 train_acc= 0.27687 val_loss= 1.57625 val_acc= 0.33333 time= 0.02501
Epoch: 0003 train_loss= 1.65564 train_acc= 0.27036 val_loss= 1.53614 val_acc= 0.35000 time= 0.02501
Epoch: 0004 train_loss= 1.93056 train_acc= 0.30293 val_loss= 1.51118 val_acc= 0.35000 time= 0.02301
Epoch: 0005 train_loss= 1.56850 train_acc= 0.27687 val_loss= 1.50019 val_acc= 0.35000 time= 0.02701
Epoch: 0006 train_loss= 1.81941 train_acc= 0.29642 val_loss= 1.42125 val_acc= 0.35000 time= 0.02901
Epoch: 0007 train_loss= 1.65965 train_acc= 0.28664 val_loss= 1.37033 val_acc= 0.31667 time= 0.02501
Epoch: 0008 train_loss= 1.47116 train_acc= 0.26059 val_loss= 1.37617 val_acc= 0.26667 time= 0.02300
Epoch: 0009 train_loss= 1.42378 train_acc= 0.28664 val_loss= 1.39872 val_acc= 0.20000 time= 0.02501
Epoch: 0010 train_loss= 1.47149 train_acc= 0.28664 val_loss= 1.42047 val_acc= 0.26667 time= 0.02400
Epoch: 0011 train_loss= 1.39986 train_acc= 0.25407 val_loss= 1.43265 val_acc= 0.23333 time= 0.02114
Epoch: 0012 train_loss= 1.39230 train_acc= 0.30619 val_loss= 1.44442 val_acc= 0.20000 time= 0.01563
Epoch: 0013 train_loss= 1.46064 train_acc= 0.27362 val_loss= 1.44149 val_acc= 0.20000 time= 0.02948
Early stopping...
Optimization Finished!
Test set results: cost= 1.50117 accuracy= 0.25000 time= 0.01562 
