Epoch: 0001 train_loss= 1.38368 train_acc= 0.28212 val_loss= 1.38329 val_acc= 0.32143 time= 0.89110
Epoch: 0002 train_loss= 1.38300 train_acc= 0.29330 val_loss= 1.38246 val_acc= 0.33929 time= 0.00000
Epoch: 0003 train_loss= 1.38179 train_acc= 0.30587 val_loss= 1.38164 val_acc= 0.33929 time= 0.01563
Epoch: 0004 train_loss= 1.38167 train_acc= 0.28911 val_loss= 1.38085 val_acc= 0.33929 time= 0.00000
Epoch: 0005 train_loss= 1.38111 train_acc= 0.31704 val_loss= 1.38008 val_acc= 0.33929 time= 0.00000
Epoch: 0006 train_loss= 1.37997 train_acc= 0.30307 val_loss= 1.37934 val_acc= 0.33929 time= 0.01563
Epoch: 0007 train_loss= 1.37936 train_acc= 0.30447 val_loss= 1.37862 val_acc= 0.33929 time= 0.00000
Epoch: 0008 train_loss= 1.37818 train_acc= 0.30447 val_loss= 1.37793 val_acc= 0.33929 time= 0.01563
Epoch: 0009 train_loss= 1.37742 train_acc= 0.30726 val_loss= 1.37727 val_acc= 0.33929 time= 0.00000
Epoch: 0010 train_loss= 1.37747 train_acc= 0.30726 val_loss= 1.37664 val_acc= 0.33929 time= 0.01563
Epoch: 0011 train_loss= 1.37605 train_acc= 0.30726 val_loss= 1.37605 val_acc= 0.33929 time= 0.00000
Epoch: 0012 train_loss= 1.37539 train_acc= 0.30447 val_loss= 1.37551 val_acc= 0.33929 time= 0.01563
Epoch: 0013 train_loss= 1.37489 train_acc= 0.30726 val_loss= 1.37501 val_acc= 0.33929 time= 0.00000
Epoch: 0014 train_loss= 1.37445 train_acc= 0.30866 val_loss= 1.37459 val_acc= 0.33929 time= 0.00000
Epoch: 0015 train_loss= 1.37548 train_acc= 0.30726 val_loss= 1.37424 val_acc= 0.33929 time= 0.01563
Epoch: 0016 train_loss= 1.37465 train_acc= 0.30726 val_loss= 1.37397 val_acc= 0.33929 time= 0.00000
Epoch: 0017 train_loss= 1.37608 train_acc= 0.30726 val_loss= 1.37379 val_acc= 0.33929 time= 0.01563
Epoch: 0018 train_loss= 1.37536 train_acc= 0.30726 val_loss= 1.37367 val_acc= 0.33929 time= 0.00000
Epoch: 0019 train_loss= 1.37716 train_acc= 0.30587 val_loss= 1.37360 val_acc= 0.33929 time= 0.01563
Epoch: 0020 train_loss= 1.37515 train_acc= 0.30726 val_loss= 1.37356 val_acc= 0.33929 time= 0.00000
Epoch: 0021 train_loss= 1.37434 train_acc= 0.30726 val_loss= 1.37354 val_acc= 0.33929 time= 0.00000
Epoch: 0022 train_loss= 1.37551 train_acc= 0.30726 val_loss= 1.37354 val_acc= 0.33929 time= 0.01563
Epoch: 0023 train_loss= 1.37471 train_acc= 0.30726 val_loss= 1.37353 val_acc= 0.33929 time= 0.00000
Epoch: 0024 train_loss= 1.37515 train_acc= 0.30726 val_loss= 1.37356 val_acc= 0.33929 time= 0.01563
Epoch: 0025 train_loss= 1.37651 train_acc= 0.30726 val_loss= 1.37359 val_acc= 0.33929 time= 0.00000
Epoch: 0026 train_loss= 1.37462 train_acc= 0.30726 val_loss= 1.37362 val_acc= 0.33929 time= 0.01563
Epoch: 0027 train_loss= 1.37429 train_acc= 0.30726 val_loss= 1.37364 val_acc= 0.33929 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 1.37741 accuracy= 0.29204 time= 0.00000 
