Epoch: 0001 train_loss= 2.08742 train_acc= 0.09434 val_loss= 2.08498 val_acc= 0.27586 time= 0.46488
Epoch: 0002 train_loss= 2.08501 train_acc= 0.15849 val_loss= 2.08223 val_acc= 0.27586 time= 0.01563
Epoch: 0003 train_loss= 2.08268 train_acc= 0.16604 val_loss= 2.07985 val_acc= 0.27586 time= 0.00000
Epoch: 0004 train_loss= 2.08073 train_acc= 0.17358 val_loss= 2.07766 val_acc= 0.27586 time= 0.01563
Epoch: 0005 train_loss= 2.07911 train_acc= 0.18113 val_loss= 2.07570 val_acc= 0.27586 time= 0.00000
Epoch: 0006 train_loss= 2.07757 train_acc= 0.16981 val_loss= 2.07389 val_acc= 0.27586 time= 0.01563
Epoch: 0007 train_loss= 2.07663 train_acc= 0.17358 val_loss= 2.07207 val_acc= 0.27586 time= 0.00000
Epoch: 0008 train_loss= 2.07519 train_acc= 0.16604 val_loss= 2.07027 val_acc= 0.27586 time= 0.01563
Epoch: 0009 train_loss= 2.07409 train_acc= 0.17358 val_loss= 2.06844 val_acc= 0.27586 time= 0.00000
Epoch: 0010 train_loss= 2.07257 train_acc= 0.17358 val_loss= 2.06657 val_acc= 0.27586 time= 0.01563
Epoch: 0011 train_loss= 2.07163 train_acc= 0.17358 val_loss= 2.06474 val_acc= 0.27586 time= 0.00000
Epoch: 0012 train_loss= 2.07101 train_acc= 0.16981 val_loss= 2.06288 val_acc= 0.27586 time= 0.01563
Epoch: 0013 train_loss= 2.07047 train_acc= 0.16981 val_loss= 2.06124 val_acc= 0.27586 time= 0.00000
Epoch: 0014 train_loss= 2.06952 train_acc= 0.16604 val_loss= 2.05968 val_acc= 0.27586 time= 0.00000
Epoch: 0015 train_loss= 2.06859 train_acc= 0.16981 val_loss= 2.05806 val_acc= 0.27586 time= 0.01563
Epoch: 0016 train_loss= 2.06839 train_acc= 0.17358 val_loss= 2.05645 val_acc= 0.27586 time= 0.00000
Epoch: 0017 train_loss= 2.06871 train_acc= 0.16981 val_loss= 2.05491 val_acc= 0.27586 time= 0.01563
Epoch: 0018 train_loss= 2.06700 train_acc= 0.16981 val_loss= 2.05356 val_acc= 0.27586 time= 0.00000
Epoch: 0019 train_loss= 2.06703 train_acc= 0.17358 val_loss= 2.05219 val_acc= 0.27586 time= 0.01563
Epoch: 0020 train_loss= 2.06712 train_acc= 0.17736 val_loss= 2.05105 val_acc= 0.27586 time= 0.00000
Epoch: 0021 train_loss= 2.06701 train_acc= 0.17358 val_loss= 2.05006 val_acc= 0.27586 time= 0.01563
Epoch: 0022 train_loss= 2.06636 train_acc= 0.17358 val_loss= 2.04936 val_acc= 0.27586 time= 0.00000
Epoch: 0023 train_loss= 2.06545 train_acc= 0.17358 val_loss= 2.04886 val_acc= 0.27586 time= 0.01563
Epoch: 0024 train_loss= 2.06503 train_acc= 0.16604 val_loss= 2.04836 val_acc= 0.27586 time= 0.00000
Epoch: 0025 train_loss= 2.06412 train_acc= 0.16981 val_loss= 2.04801 val_acc= 0.27586 time= 0.01563
Epoch: 0026 train_loss= 2.06433 train_acc= 0.17736 val_loss= 2.04753 val_acc= 0.27586 time= 0.00000
Epoch: 0027 train_loss= 2.06437 train_acc= 0.17358 val_loss= 2.04698 val_acc= 0.27586 time= 0.01563
Epoch: 0028 train_loss= 2.06248 train_acc= 0.18113 val_loss= 2.04627 val_acc= 0.27586 time= 0.00000
Epoch: 0029 train_loss= 2.06537 train_acc= 0.16604 val_loss= 2.04566 val_acc= 0.27586 time= 0.01563
Epoch: 0030 train_loss= 2.06481 train_acc= 0.17358 val_loss= 2.04489 val_acc= 0.27586 time= 0.00000
Epoch: 0031 train_loss= 2.06237 train_acc= 0.17358 val_loss= 2.04426 val_acc= 0.27586 time= 0.01563
Epoch: 0032 train_loss= 2.06211 train_acc= 0.16981 val_loss= 2.04383 val_acc= 0.27586 time= 0.00000
Epoch: 0033 train_loss= 2.06148 train_acc= 0.16981 val_loss= 2.04366 val_acc= 0.27586 time= 0.01860
Epoch: 0034 train_loss= 2.06034 train_acc= 0.17736 val_loss= 2.04329 val_acc= 0.27586 time= 0.00202
Epoch: 0035 train_loss= 2.06366 train_acc= 0.17358 val_loss= 2.04291 val_acc= 0.27586 time= 0.01100
Epoch: 0036 train_loss= 2.06345 train_acc= 0.16981 val_loss= 2.04260 val_acc= 0.27586 time= 0.00000
Epoch: 0037 train_loss= 2.06091 train_acc= 0.16981 val_loss= 2.04213 val_acc= 0.27586 time= 0.01563
Epoch: 0038 train_loss= 2.06176 train_acc= 0.16981 val_loss= 2.04178 val_acc= 0.27586 time= 0.00000
Epoch: 0039 train_loss= 2.05992 train_acc= 0.17358 val_loss= 2.04163 val_acc= 0.27586 time= 0.00000
Epoch: 0040 train_loss= 2.06040 train_acc= 0.17736 val_loss= 2.04169 val_acc= 0.27586 time= 0.01563
Epoch: 0041 train_loss= 2.06008 train_acc= 0.17736 val_loss= 2.04188 val_acc= 0.27586 time= 0.00000
Epoch: 0042 train_loss= 2.06077 train_acc= 0.16981 val_loss= 2.04208 val_acc= 0.27586 time= 0.01563
Epoch: 0043 train_loss= 2.06001 train_acc= 0.16981 val_loss= 2.04202 val_acc= 0.27586 time= 0.00000
Epoch: 0044 train_loss= 2.05981 train_acc= 0.16981 val_loss= 2.04142 val_acc= 0.27586 time= 0.01563
Epoch: 0045 train_loss= 2.05735 train_acc= 0.17736 val_loss= 2.04077 val_acc= 0.27586 time= 0.00000
Epoch: 0046 train_loss= 2.06158 train_acc= 0.16981 val_loss= 2.03981 val_acc= 0.27586 time= 0.01563
Epoch: 0047 train_loss= 2.05840 train_acc= 0.17736 val_loss= 2.03898 val_acc= 0.27586 time= 0.00000
Epoch: 0048 train_loss= 2.05871 train_acc= 0.17736 val_loss= 2.03824 val_acc= 0.27586 time= 0.01563
Epoch: 0049 train_loss= 2.06000 train_acc= 0.16981 val_loss= 2.03759 val_acc= 0.27586 time= 0.00000
Epoch: 0050 train_loss= 2.06056 train_acc= 0.17358 val_loss= 2.03749 val_acc= 0.27586 time= 0.01563
Epoch: 0051 train_loss= 2.05952 train_acc= 0.17358 val_loss= 2.03775 val_acc= 0.27586 time= 0.00000
Epoch: 0052 train_loss= 2.05781 train_acc= 0.17736 val_loss= 2.03789 val_acc= 0.27586 time= 0.00000
Epoch: 0053 train_loss= 2.05874 train_acc= 0.18113 val_loss= 2.03798 val_acc= 0.27586 time= 0.01563
Epoch: 0054 train_loss= 2.05649 train_acc= 0.17736 val_loss= 2.03852 val_acc= 0.27586 time= 0.00000
Epoch: 0055 train_loss= 2.05822 train_acc= 0.17736 val_loss= 2.03915 val_acc= 0.27586 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 2.07604 accuracy= 0.16949 time= 0.00000 
