Epoch: 0001 train_loss= 2.08669 train_acc= 0.09057 val_loss= 2.07645 val_acc= 0.13793 time= 0.54580
Epoch: 0002 train_loss= 2.08447 train_acc= 0.09057 val_loss= 2.07526 val_acc= 0.17241 time= 0.00000
Epoch: 0003 train_loss= 2.08212 train_acc= 0.13585 val_loss= 2.07410 val_acc= 0.13793 time= 0.01562
Epoch: 0004 train_loss= 2.08272 train_acc= 0.10566 val_loss= 2.07298 val_acc= 0.17241 time= 0.00000
Epoch: 0005 train_loss= 2.08185 train_acc= 0.14340 val_loss= 2.07198 val_acc= 0.17241 time= 0.00000
Epoch: 0006 train_loss= 2.07883 train_acc= 0.14340 val_loss= 2.07094 val_acc= 0.13793 time= 0.01563
Epoch: 0007 train_loss= 2.07819 train_acc= 0.14717 val_loss= 2.06990 val_acc= 0.10345 time= 0.00000
Epoch: 0008 train_loss= 2.07684 train_acc= 0.16226 val_loss= 2.06881 val_acc= 0.06897 time= 0.00000
Epoch: 0009 train_loss= 2.07619 train_acc= 0.13585 val_loss= 2.06769 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.07616 train_acc= 0.12453 val_loss= 2.06652 val_acc= 0.10345 time= 0.00000
Epoch: 0011 train_loss= 2.07412 train_acc= 0.14340 val_loss= 2.06529 val_acc= 0.10345 time= 0.00000
Epoch: 0012 train_loss= 2.07409 train_acc= 0.15472 val_loss= 2.06401 val_acc= 0.10345 time= 0.01563
Epoch: 0013 train_loss= 2.07004 train_acc= 0.18868 val_loss= 2.06271 val_acc= 0.10345 time= 0.00000
Epoch: 0014 train_loss= 2.06849 train_acc= 0.13585 val_loss= 2.06132 val_acc= 0.10345 time= 0.00000
Epoch: 0015 train_loss= 2.07068 train_acc= 0.13585 val_loss= 2.05984 val_acc= 0.10345 time= 0.01563
Epoch: 0016 train_loss= 2.06771 train_acc= 0.13208 val_loss= 2.05836 val_acc= 0.10345 time= 0.00000
Epoch: 0017 train_loss= 2.06762 train_acc= 0.14340 val_loss= 2.05689 val_acc= 0.10345 time= 0.00000
Epoch: 0018 train_loss= 2.06647 train_acc= 0.14717 val_loss= 2.05542 val_acc= 0.10345 time= 0.01563
Epoch: 0019 train_loss= 2.06537 train_acc= 0.15472 val_loss= 2.05405 val_acc= 0.10345 time= 0.00000
Epoch: 0020 train_loss= 2.06399 train_acc= 0.13962 val_loss= 2.05274 val_acc= 0.10345 time= 0.01563
Epoch: 0021 train_loss= 2.06309 train_acc= 0.14717 val_loss= 2.05156 val_acc= 0.10345 time= 0.00000
Epoch: 0022 train_loss= 2.06665 train_acc= 0.15849 val_loss= 2.05062 val_acc= 0.10345 time= 0.00000
Epoch: 0023 train_loss= 2.06082 train_acc= 0.14717 val_loss= 2.05002 val_acc= 0.10345 time= 0.01563
Epoch: 0024 train_loss= 2.06139 train_acc= 0.13585 val_loss= 2.05009 val_acc= 0.10345 time= 0.00000
Epoch: 0025 train_loss= 2.06107 train_acc= 0.16226 val_loss= 2.05024 val_acc= 0.10345 time= 0.00000
Epoch: 0026 train_loss= 2.06181 train_acc= 0.14717 val_loss= 2.05049 val_acc= 0.10345 time= 0.01563
Epoch: 0027 train_loss= 2.06089 train_acc= 0.16226 val_loss= 2.05103 val_acc= 0.10345 time= 0.00000
Epoch: 0028 train_loss= 2.06212 train_acc= 0.13962 val_loss= 2.05169 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.09933 accuracy= 0.15254 time= 0.01563 
