Epoch: 0001 train_loss= 2.08359 train_acc= 0.15094 val_loss= 2.09442 val_acc= 0.13793 time= 0.84202
Epoch: 0002 train_loss= 2.08437 train_acc= 0.15094 val_loss= 2.09261 val_acc= 0.13793 time= 0.00000
Epoch: 0003 train_loss= 2.08247 train_acc= 0.15094 val_loss= 2.09083 val_acc= 0.13793 time= 0.00000
Epoch: 0004 train_loss= 2.08042 train_acc= 0.15094 val_loss= 2.08902 val_acc= 0.13793 time= 0.00000
Epoch: 0005 train_loss= 2.07927 train_acc= 0.15094 val_loss= 2.08722 val_acc= 0.13793 time= 0.01563
Epoch: 0006 train_loss= 2.07808 train_acc= 0.15094 val_loss= 2.08543 val_acc= 0.13793 time= 0.00000
Epoch: 0007 train_loss= 2.07784 train_acc= 0.15094 val_loss= 2.08364 val_acc= 0.13793 time= 0.00000
Epoch: 0008 train_loss= 2.07651 train_acc= 0.15094 val_loss= 2.08185 val_acc= 0.13793 time= 0.01563
Epoch: 0009 train_loss= 2.07360 train_acc= 0.15094 val_loss= 2.08006 val_acc= 0.13793 time= 0.00000
Epoch: 0010 train_loss= 2.07247 train_acc= 0.14286 val_loss= 2.07826 val_acc= 0.13793 time= 0.00000
Epoch: 0011 train_loss= 2.07154 train_acc= 0.15094 val_loss= 2.07651 val_acc= 0.13793 time= 0.01563
Epoch: 0012 train_loss= 2.07055 train_acc= 0.15094 val_loss= 2.07477 val_acc= 0.13793 time= 0.00000
Epoch: 0013 train_loss= 2.06835 train_acc= 0.15094 val_loss= 2.07307 val_acc= 0.13793 time= 0.00000
Epoch: 0014 train_loss= 2.06669 train_acc= 0.15094 val_loss= 2.07148 val_acc= 0.13793 time= 0.01563
Epoch: 0015 train_loss= 2.06540 train_acc= 0.15364 val_loss= 2.07001 val_acc= 0.13793 time= 0.00000
Epoch: 0016 train_loss= 2.06439 train_acc= 0.16981 val_loss= 2.06867 val_acc= 0.13793 time= 0.00000
Epoch: 0017 train_loss= 2.06458 train_acc= 0.13477 val_loss= 2.06747 val_acc= 0.13793 time= 0.01563
Epoch: 0018 train_loss= 2.06259 train_acc= 0.13747 val_loss= 2.06647 val_acc= 0.13793 time= 0.00000
Epoch: 0019 train_loss= 2.06298 train_acc= 0.13477 val_loss= 2.06569 val_acc= 0.13793 time= 0.00000
Epoch: 0020 train_loss= 2.06155 train_acc= 0.16712 val_loss= 2.06513 val_acc= 0.13793 time= 0.01563
Epoch: 0021 train_loss= 2.06034 train_acc= 0.14825 val_loss= 2.06477 val_acc= 0.17241 time= 0.00000
Epoch: 0022 train_loss= 2.06015 train_acc= 0.14286 val_loss= 2.06458 val_acc= 0.13793 time= 0.00000
Epoch: 0023 train_loss= 2.05964 train_acc= 0.14555 val_loss= 2.06458 val_acc= 0.10345 time= 0.01563
Epoch: 0024 train_loss= 2.05978 train_acc= 0.14286 val_loss= 2.06478 val_acc= 0.13793 time= 0.00000
Epoch: 0025 train_loss= 2.05852 train_acc= 0.13477 val_loss= 2.06511 val_acc= 0.13793 time= 0.00000
Epoch: 0026 train_loss= 2.05937 train_acc= 0.14555 val_loss= 2.06548 val_acc= 0.13793 time= 0.01563
Epoch: 0027 train_loss= 2.05893 train_acc= 0.14555 val_loss= 2.06590 val_acc= 0.13793 time= 0.00000
Early stopping...
Optimization Finished!
Test set results: cost= 2.08046 accuracy= 0.15254 time= 0.00000 
