Epoch: 0001 train_loss= 2.08514 train_acc= 0.10189 val_loss= 2.08128 val_acc= 0.17241 time= 0.20314
Epoch: 0002 train_loss= 2.08376 train_acc= 0.10943 val_loss= 2.08068 val_acc= 0.06897 time= 0.01563
Epoch: 0003 train_loss= 2.08226 train_acc= 0.18113 val_loss= 2.07970 val_acc= 0.06897 time= 0.00000
Epoch: 0004 train_loss= 2.08133 train_acc= 0.17736 val_loss= 2.07829 val_acc= 0.06897 time= 0.01563
Epoch: 0005 train_loss= 2.07989 train_acc= 0.17736 val_loss= 2.07665 val_acc= 0.06897 time= 0.00000
Epoch: 0006 train_loss= 2.07896 train_acc= 0.18113 val_loss= 2.07465 val_acc= 0.06897 time= 0.01563
Epoch: 0007 train_loss= 2.07744 train_acc= 0.18113 val_loss= 2.07239 val_acc= 0.06897 time= 0.01563
Epoch: 0008 train_loss= 2.07553 train_acc= 0.18113 val_loss= 2.06984 val_acc= 0.06897 time= 0.00000
Epoch: 0009 train_loss= 2.07398 train_acc= 0.18113 val_loss= 2.06716 val_acc= 0.06897 time= 0.01563
Epoch: 0010 train_loss= 2.07269 train_acc= 0.18113 val_loss= 2.06437 val_acc= 0.06897 time= 0.00000
Epoch: 0011 train_loss= 2.07171 train_acc= 0.18113 val_loss= 2.06150 val_acc= 0.06897 time= 0.01563
Epoch: 0012 train_loss= 2.07072 train_acc= 0.18113 val_loss= 2.05874 val_acc= 0.06897 time= 0.01563
Epoch: 0013 train_loss= 2.06966 train_acc= 0.18113 val_loss= 2.05618 val_acc= 0.06897 time= 0.00000
Epoch: 0014 train_loss= 2.06755 train_acc= 0.18113 val_loss= 2.05387 val_acc= 0.06897 time= 0.01563
Epoch: 0015 train_loss= 2.06606 train_acc= 0.18113 val_loss= 2.05181 val_acc= 0.06897 time= 0.00000
Epoch: 0016 train_loss= 2.06564 train_acc= 0.18113 val_loss= 2.05021 val_acc= 0.06897 time= 0.01563
Epoch: 0017 train_loss= 2.06468 train_acc= 0.18113 val_loss= 2.04910 val_acc= 0.06897 time= 0.00000
Epoch: 0018 train_loss= 2.06281 train_acc= 0.18113 val_loss= 2.04866 val_acc= 0.06897 time= 0.01562
Epoch: 0019 train_loss= 2.06055 train_acc= 0.18113 val_loss= 2.04858 val_acc= 0.06897 time= 0.01563
Epoch: 0020 train_loss= 2.06124 train_acc= 0.18113 val_loss= 2.04888 val_acc= 0.06897 time= 0.00000
Epoch: 0021 train_loss= 2.05861 train_acc= 0.18113 val_loss= 2.04939 val_acc= 0.06897 time= 0.01563
Epoch: 0022 train_loss= 2.05812 train_acc= 0.18113 val_loss= 2.05023 val_acc= 0.06897 time= 0.00000
Epoch: 0023 train_loss= 2.05743 train_acc= 0.18113 val_loss= 2.05126 val_acc= 0.06897 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.04889 accuracy= 0.11864 time= 0.00000 
