Epoch: 0001 train_loss= 2.08476 train_acc= 0.16442 val_loss= 2.07490 val_acc= 0.24138 time= 0.65629
Epoch: 0002 train_loss= 2.08284 train_acc= 0.16442 val_loss= 2.07070 val_acc= 0.24138 time= 0.00000
Epoch: 0003 train_loss= 2.08164 train_acc= 0.15364 val_loss= 2.06716 val_acc= 0.24138 time= 0.01563
Epoch: 0004 train_loss= 2.08023 train_acc= 0.17790 val_loss= 2.06360 val_acc= 0.24138 time= 0.01563
Epoch: 0005 train_loss= 2.07930 train_acc= 0.16442 val_loss= 2.05987 val_acc= 0.24138 time= 0.00000
Epoch: 0006 train_loss= 2.07796 train_acc= 0.15903 val_loss= 2.05603 val_acc= 0.24138 time= 0.01563
Epoch: 0007 train_loss= 2.07687 train_acc= 0.17251 val_loss= 2.05200 val_acc= 0.24138 time= 0.00000
Epoch: 0008 train_loss= 2.07557 train_acc= 0.16712 val_loss= 2.04788 val_acc= 0.24138 time= 0.01563
Epoch: 0009 train_loss= 2.07436 train_acc= 0.16442 val_loss= 2.04378 val_acc= 0.24138 time= 0.00000
Epoch: 0010 train_loss= 2.07355 train_acc= 0.16712 val_loss= 2.03987 val_acc= 0.24138 time= 0.01563
Epoch: 0011 train_loss= 2.07340 train_acc= 0.16442 val_loss= 2.03594 val_acc= 0.24138 time= 0.00000
Epoch: 0012 train_loss= 2.07114 train_acc= 0.16712 val_loss= 2.03184 val_acc= 0.24138 time= 0.01563
Epoch: 0013 train_loss= 2.07054 train_acc= 0.16712 val_loss= 2.02788 val_acc= 0.24138 time= 0.00000
Epoch: 0014 train_loss= 2.06931 train_acc= 0.16981 val_loss= 2.02421 val_acc= 0.24138 time= 0.01563
Epoch: 0015 train_loss= 2.06955 train_acc= 0.16442 val_loss= 2.02081 val_acc= 0.24138 time= 0.00000
Epoch: 0016 train_loss= 2.06588 train_acc= 0.16173 val_loss= 2.01776 val_acc= 0.24138 time= 0.01563
Epoch: 0017 train_loss= 2.06742 train_acc= 0.16173 val_loss= 2.01508 val_acc= 0.24138 time= 0.00000
Epoch: 0018 train_loss= 2.06780 train_acc= 0.16442 val_loss= 2.01275 val_acc= 0.24138 time= 0.01563
Epoch: 0019 train_loss= 2.06768 train_acc= 0.16173 val_loss= 2.01084 val_acc= 0.24138 time= 0.01563
Epoch: 0020 train_loss= 2.06662 train_acc= 0.16442 val_loss= 2.00937 val_acc= 0.24138 time= 0.00000
Epoch: 0021 train_loss= 2.06520 train_acc= 0.16442 val_loss= 2.00808 val_acc= 0.24138 time= 0.01563
Epoch: 0022 train_loss= 2.06593 train_acc= 0.16442 val_loss= 2.00721 val_acc= 0.24138 time= 0.00000
Epoch: 0023 train_loss= 2.06685 train_acc= 0.16442 val_loss= 2.00677 val_acc= 0.24138 time= 0.01563
Epoch: 0024 train_loss= 2.06516 train_acc= 0.16173 val_loss= 2.00563 val_acc= 0.24138 time= 0.00000
Epoch: 0025 train_loss= 2.06498 train_acc= 0.16442 val_loss= 2.00492 val_acc= 0.24138 time= 0.01563
Epoch: 0026 train_loss= 2.06531 train_acc= 0.16442 val_loss= 2.00432 val_acc= 0.24138 time= 0.00000
Epoch: 0027 train_loss= 2.06490 train_acc= 0.16442 val_loss= 2.00387 val_acc= 0.24138 time= 0.01563
Epoch: 0028 train_loss= 2.06591 train_acc= 0.16442 val_loss= 2.00374 val_acc= 0.24138 time= 0.00000
Epoch: 0029 train_loss= 2.06449 train_acc= 0.16712 val_loss= 2.00365 val_acc= 0.24138 time= 0.01563
Epoch: 0030 train_loss= 2.06450 train_acc= 0.16442 val_loss= 2.00385 val_acc= 0.24138 time= 0.00000
Epoch: 0031 train_loss= 2.06520 train_acc= 0.16442 val_loss= 2.00418 val_acc= 0.24138 time= 0.01563
Epoch: 0032 train_loss= 2.06329 train_acc= 0.16173 val_loss= 2.00466 val_acc= 0.24138 time= 0.00000
Epoch: 0033 train_loss= 2.06260 train_acc= 0.16442 val_loss= 2.00524 val_acc= 0.24138 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 2.03293 accuracy= 0.11864 time= 0.00000 
