Epoch: 0001 train_loss= 1.39257 train_acc= 0.24302 val_loss= 1.38949 val_acc= 0.35714 time= 0.65629
Epoch: 0002 train_loss= 1.39081 train_acc= 0.30307 val_loss= 1.38786 val_acc= 0.35714 time= 0.01563
Epoch: 0003 train_loss= 1.38945 train_acc= 0.29888 val_loss= 1.38636 val_acc= 0.35714 time= 0.01563
Epoch: 0004 train_loss= 1.38880 train_acc= 0.30168 val_loss= 1.38482 val_acc= 0.35714 time= 0.01563
Epoch: 0005 train_loss= 1.38725 train_acc= 0.30168 val_loss= 1.38325 val_acc= 0.35714 time= 0.00000
Epoch: 0006 train_loss= 1.38602 train_acc= 0.30168 val_loss= 1.38167 val_acc= 0.35714 time= 0.01563
Epoch: 0007 train_loss= 1.38541 train_acc= 0.30168 val_loss= 1.38011 val_acc= 0.35714 time= 0.01563
Epoch: 0008 train_loss= 1.38460 train_acc= 0.30168 val_loss= 1.37855 val_acc= 0.35714 time= 0.01563
Epoch: 0009 train_loss= 1.38400 train_acc= 0.30168 val_loss= 1.37701 val_acc= 0.35714 time= 0.01563
Epoch: 0010 train_loss= 1.38246 train_acc= 0.30168 val_loss= 1.37547 val_acc= 0.35714 time= 0.01563
Epoch: 0011 train_loss= 1.38220 train_acc= 0.30168 val_loss= 1.37391 val_acc= 0.35714 time= 0.01563
Epoch: 0012 train_loss= 1.38119 train_acc= 0.30168 val_loss= 1.37238 val_acc= 0.35714 time= 0.01563
Epoch: 0013 train_loss= 1.38086 train_acc= 0.30168 val_loss= 1.37083 val_acc= 0.35714 time= 0.01563
Epoch: 0014 train_loss= 1.38033 train_acc= 0.30168 val_loss= 1.36927 val_acc= 0.35714 time= 0.01563
Epoch: 0015 train_loss= 1.37964 train_acc= 0.30168 val_loss= 1.36768 val_acc= 0.35714 time= 0.01563
Epoch: 0016 train_loss= 1.37978 train_acc= 0.30307 val_loss= 1.36612 val_acc= 0.35714 time= 0.01563
Epoch: 0017 train_loss= 1.37982 train_acc= 0.30168 val_loss= 1.36463 val_acc= 0.35714 time= 0.01563
Epoch: 0018 train_loss= 1.37903 train_acc= 0.30168 val_loss= 1.36323 val_acc= 0.35714 time= 0.01563
Epoch: 0019 train_loss= 1.37881 train_acc= 0.30168 val_loss= 1.36194 val_acc= 0.35714 time= 0.00000
Epoch: 0020 train_loss= 1.37787 train_acc= 0.30168 val_loss= 1.36083 val_acc= 0.35714 time= 0.01563
Epoch: 0021 train_loss= 1.37857 train_acc= 0.30168 val_loss= 1.35993 val_acc= 0.35714 time= 0.01562
Epoch: 0022 train_loss= 1.37843 train_acc= 0.30168 val_loss= 1.35926 val_acc= 0.35714 time= 0.01563
Epoch: 0023 train_loss= 1.37923 train_acc= 0.30168 val_loss= 1.35882 val_acc= 0.35714 time= 0.01563
Epoch: 0024 train_loss= 1.37898 train_acc= 0.30168 val_loss= 1.35861 val_acc= 0.35714 time= 0.01563
Epoch: 0025 train_loss= 1.37872 train_acc= 0.30168 val_loss= 1.35877 val_acc= 0.35714 time= 0.01563
Epoch: 0026 train_loss= 1.37956 train_acc= 0.30168 val_loss= 1.35926 val_acc= 0.35714 time= 0.01563
Epoch: 0027 train_loss= 1.37835 train_acc= 0.30168 val_loss= 1.36007 val_acc= 0.35714 time= 0.01563
Epoch: 0028 train_loss= 1.37812 train_acc= 0.30168 val_loss= 1.36101 val_acc= 0.35714 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37236 accuracy= 0.29204 time= 0.00000 
