Epoch: 0001 train_loss= 2.73718 train_acc= 0.25781 val_loss= 1.41523 val_acc= 0.32143 time= 0.65705
Epoch: 0002 train_loss= 1.92362 train_acc= 0.28320 val_loss= 1.41827 val_acc= 0.30357 time= 0.01563
Epoch: 0003 train_loss= 2.04532 train_acc= 0.29492 val_loss= 1.48486 val_acc= 0.32143 time= 0.03125
Epoch: 0004 train_loss= 1.87414 train_acc= 0.27734 val_loss= 1.51177 val_acc= 0.32143 time= 0.01562
Epoch: 0005 train_loss= 1.95340 train_acc= 0.32031 val_loss= 1.51401 val_acc= 0.30357 time= 0.03125
Epoch: 0006 train_loss= 2.52465 train_acc= 0.29883 val_loss= 1.50046 val_acc= 0.30357 time= 0.01563
Epoch: 0007 train_loss= 1.95426 train_acc= 0.28906 val_loss= 1.48403 val_acc= 0.30357 time= 0.03125
Epoch: 0008 train_loss= 2.20831 train_acc= 0.29102 val_loss= 1.46664 val_acc= 0.32143 time= 0.01563
Epoch: 0009 train_loss= 1.99105 train_acc= 0.30664 val_loss= 1.44615 val_acc= 0.32143 time= 0.03125
Epoch: 0010 train_loss= 1.86307 train_acc= 0.26562 val_loss= 1.42943 val_acc= 0.32143 time= 0.03125
Epoch: 0011 train_loss= 2.61802 train_acc= 0.29102 val_loss= 1.41691 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.42473 train_acc= 0.27734 val_loss= 1.40931 val_acc= 0.33929 time= 0.03125
Epoch: 0013 train_loss= 1.50891 train_acc= 0.25586 val_loss= 1.40512 val_acc= 0.35714 time= 0.01563
Epoch: 0014 train_loss= 1.43674 train_acc= 0.27539 val_loss= 1.40356 val_acc= 0.33929 time= 0.03125
Epoch: 0015 train_loss= 1.46034 train_acc= 0.28711 val_loss= 1.40378 val_acc= 0.33929 time= 0.01563
Epoch: 0016 train_loss= 1.41802 train_acc= 0.28320 val_loss= 1.40360 val_acc= 0.37500 time= 0.03125
Epoch: 0017 train_loss= 1.41994 train_acc= 0.28906 val_loss= 1.40301 val_acc= 0.37500 time= 0.01563
Epoch: 0018 train_loss= 1.42349 train_acc= 0.31641 val_loss= 1.40256 val_acc= 0.37500 time= 0.03125
Epoch: 0019 train_loss= 1.39549 train_acc= 0.28320 val_loss= 1.40232 val_acc= 0.39286 time= 0.01563
Epoch: 0020 train_loss= 1.41358 train_acc= 0.29297 val_loss= 1.40191 val_acc= 0.39286 time= 0.01563
Epoch: 0021 train_loss= 1.40120 train_acc= 0.27734 val_loss= 1.40156 val_acc= 0.39286 time= 0.01563
Epoch: 0022 train_loss= 1.40942 train_acc= 0.28711 val_loss= 1.40112 val_acc= 0.35714 time= 0.03125
Epoch: 0023 train_loss= 1.41451 train_acc= 0.27344 val_loss= 1.40079 val_acc= 0.33929 time= 0.01563
Epoch: 0024 train_loss= 1.39007 train_acc= 0.29688 val_loss= 1.40060 val_acc= 0.32143 time= 0.03125
Epoch: 0025 train_loss= 1.39558 train_acc= 0.28711 val_loss= 1.40043 val_acc= 0.32143 time= 0.01562
Epoch: 0026 train_loss= 1.38517 train_acc= 0.28711 val_loss= 1.40054 val_acc= 0.32143 time= 0.01563
Epoch: 0027 train_loss= 1.38872 train_acc= 0.30078 val_loss= 1.40078 val_acc= 0.32143 time= 0.03125
Epoch: 0028 train_loss= 1.43163 train_acc= 0.25195 val_loss= 1.40100 val_acc= 0.33929 time= 0.01563
Epoch: 0029 train_loss= 1.38224 train_acc= 0.30469 val_loss= 1.40127 val_acc= 0.33929 time= 0.03125
Early stopping...
Optimization Finished!
Test set results: cost= 1.37983 accuracy= 0.26549 time= 0.00000 
