Epoch: 0001 train_loss= 1.39164 train_acc= 0.26710 val_loss= 1.38825 val_acc= 0.31667 time= 0.12472
Epoch: 0002 train_loss= 1.38944 train_acc= 0.26710 val_loss= 1.38654 val_acc= 0.31667 time= 0.01548
Epoch: 0003 train_loss= 1.38775 train_acc= 0.26710 val_loss= 1.38509 val_acc= 0.31667 time= 0.01563
Epoch: 0004 train_loss= 1.38623 train_acc= 0.26710 val_loss= 1.38383 val_acc= 0.31667 time= 0.01562
Epoch: 0005 train_loss= 1.38561 train_acc= 0.26710 val_loss= 1.38265 val_acc= 0.31667 time= 0.01563
Epoch: 0006 train_loss= 1.38402 train_acc= 0.26710 val_loss= 1.38156 val_acc= 0.31667 time= 0.01563
Epoch: 0007 train_loss= 1.38325 train_acc= 0.26710 val_loss= 1.38061 val_acc= 0.31667 time= 0.01563
Epoch: 0008 train_loss= 1.38203 train_acc= 0.26710 val_loss= 1.37981 val_acc= 0.31667 time= 0.01563
Epoch: 0009 train_loss= 1.38156 train_acc= 0.26710 val_loss= 1.37914 val_acc= 0.31667 time= 0.01563
Epoch: 0010 train_loss= 1.37926 train_acc= 0.26710 val_loss= 1.37859 val_acc= 0.31667 time= 0.01563
Epoch: 0011 train_loss= 1.37983 train_acc= 0.26710 val_loss= 1.37800 val_acc= 0.31667 time= 0.01563
Epoch: 0012 train_loss= 1.37905 train_acc= 0.26710 val_loss= 1.37747 val_acc= 0.31667 time= 0.01563
Epoch: 0013 train_loss= 1.37815 train_acc= 0.26710 val_loss= 1.37696 val_acc= 0.31667 time= 0.00000
Epoch: 0014 train_loss= 1.37761 train_acc= 0.26710 val_loss= 1.37645 val_acc= 0.31667 time= 0.01563
Epoch: 0015 train_loss= 1.37579 train_acc= 0.28013 val_loss= 1.37599 val_acc= 0.26667 time= 0.01563
Epoch: 0016 train_loss= 1.37543 train_acc= 0.32248 val_loss= 1.37551 val_acc= 0.26667 time= 0.01563
Epoch: 0017 train_loss= 1.37501 train_acc= 0.31596 val_loss= 1.37511 val_acc= 0.26667 time= 0.01562
Epoch: 0018 train_loss= 1.37454 train_acc= 0.31596 val_loss= 1.37474 val_acc= 0.26667 time= 0.01563
Epoch: 0019 train_loss= 1.37443 train_acc= 0.31596 val_loss= 1.37441 val_acc= 0.26667 time= 0.02531
Epoch: 0020 train_loss= 1.37387 train_acc= 0.31596 val_loss= 1.37416 val_acc= 0.26667 time= 0.00806
Epoch: 0021 train_loss= 1.37258 train_acc= 0.31596 val_loss= 1.37400 val_acc= 0.26667 time= 0.02183
Epoch: 0022 train_loss= 1.37492 train_acc= 0.31596 val_loss= 1.37390 val_acc= 0.26667 time= 0.01200
Epoch: 0023 train_loss= 1.37343 train_acc= 0.31596 val_loss= 1.37384 val_acc= 0.26667 time= 0.01852
Epoch: 0024 train_loss= 1.37336 train_acc= 0.31596 val_loss= 1.37383 val_acc= 0.26667 time= 0.01600
Epoch: 0025 train_loss= 1.37394 train_acc= 0.31596 val_loss= 1.37383 val_acc= 0.26667 time= 0.00000
Epoch: 0026 train_loss= 1.37290 train_acc= 0.31596 val_loss= 1.37384 val_acc= 0.26667 time= 0.02971
Epoch: 0027 train_loss= 1.37434 train_acc= 0.31596 val_loss= 1.37375 val_acc= 0.26667 time= 0.00505
Epoch: 0028 train_loss= 1.37379 train_acc= 0.31596 val_loss= 1.37364 val_acc= 0.26667 time= 0.02564
Epoch: 0029 train_loss= 1.37299 train_acc= 0.31596 val_loss= 1.37346 val_acc= 0.26667 time= 0.00915
Epoch: 0030 train_loss= 1.37343 train_acc= 0.31596 val_loss= 1.37329 val_acc= 0.26667 time= 0.02070
Epoch: 0031 train_loss= 1.37242 train_acc= 0.31596 val_loss= 1.37336 val_acc= 0.26667 time= 0.01400
Epoch: 0032 train_loss= 1.37069 train_acc= 0.31596 val_loss= 1.37343 val_acc= 0.26667 time= 0.01568
Epoch: 0033 train_loss= 1.37187 train_acc= 0.31596 val_loss= 1.37358 val_acc= 0.26667 time= 0.01515
Epoch: 0034 train_loss= 1.37275 train_acc= 0.31596 val_loss= 1.37387 val_acc= 0.26667 time= 0.00400
Early stopping...
Optimization Finished!
Test set results: cost= 1.37649 accuracy= 0.31667 time= 0.01567 
