Epoch: 0001 train_loss= 1.39495 train_acc= 0.19531 val_loss= 1.39055 val_acc= 0.33929 time= 0.23493
Epoch: 0002 train_loss= 1.39272 train_acc= 0.22461 val_loss= 1.39009 val_acc= 0.26786 time= 0.01508
Epoch: 0003 train_loss= 1.39128 train_acc= 0.30078 val_loss= 1.38971 val_acc= 0.26786 time= 0.01563
Epoch: 0004 train_loss= 1.39010 train_acc= 0.30273 val_loss= 1.38928 val_acc= 0.26786 time= 0.01563
Epoch: 0005 train_loss= 1.38912 train_acc= 0.30273 val_loss= 1.38866 val_acc= 0.26786 time= 0.01563
Epoch: 0006 train_loss= 1.38824 train_acc= 0.30273 val_loss= 1.38805 val_acc= 0.26786 time= 0.01563
Epoch: 0007 train_loss= 1.38730 train_acc= 0.30273 val_loss= 1.38738 val_acc= 0.26786 time= 0.01563
Epoch: 0008 train_loss= 1.38641 train_acc= 0.30273 val_loss= 1.38669 val_acc= 0.26786 time= 0.01563
Epoch: 0009 train_loss= 1.38529 train_acc= 0.30273 val_loss= 1.38598 val_acc= 0.26786 time= 0.01563
Epoch: 0010 train_loss= 1.38453 train_acc= 0.30273 val_loss= 1.38524 val_acc= 0.26786 time= 0.01563
Epoch: 0011 train_loss= 1.38361 train_acc= 0.30273 val_loss= 1.38447 val_acc= 0.26786 time= 0.01563
Epoch: 0012 train_loss= 1.38250 train_acc= 0.30273 val_loss= 1.38365 val_acc= 0.26786 time= 0.01563
Epoch: 0013 train_loss= 1.38195 train_acc= 0.30273 val_loss= 1.38287 val_acc= 0.26786 time= 0.01563
Epoch: 0014 train_loss= 1.38054 train_acc= 0.30273 val_loss= 1.38218 val_acc= 0.26786 time= 0.01563
Epoch: 0015 train_loss= 1.38013 train_acc= 0.30273 val_loss= 1.38149 val_acc= 0.26786 time= 0.01563
Epoch: 0016 train_loss= 1.37962 train_acc= 0.30273 val_loss= 1.38086 val_acc= 0.26786 time= 0.01563
Epoch: 0017 train_loss= 1.37937 train_acc= 0.30273 val_loss= 1.38018 val_acc= 0.26786 time= 0.01563
Epoch: 0018 train_loss= 1.37930 train_acc= 0.30273 val_loss= 1.37956 val_acc= 0.26786 time= 0.01563
Epoch: 0019 train_loss= 1.37750 train_acc= 0.30273 val_loss= 1.37899 val_acc= 0.26786 time= 0.01563
Epoch: 0020 train_loss= 1.37861 train_acc= 0.30273 val_loss= 1.37846 val_acc= 0.26786 time= 0.01563
Epoch: 0021 train_loss= 1.37777 train_acc= 0.30273 val_loss= 1.37807 val_acc= 0.26786 time= 0.01563
Epoch: 0022 train_loss= 1.37807 train_acc= 0.30273 val_loss= 1.37781 val_acc= 0.26786 time= 0.01563
Epoch: 0023 train_loss= 1.37725 train_acc= 0.30273 val_loss= 1.37764 val_acc= 0.26786 time= 0.01562
Epoch: 0024 train_loss= 1.37822 train_acc= 0.30273 val_loss= 1.37750 val_acc= 0.26786 time= 0.01563
Epoch: 0025 train_loss= 1.37766 train_acc= 0.30273 val_loss= 1.37736 val_acc= 0.26786 time= 0.01563
Epoch: 0026 train_loss= 1.37735 train_acc= 0.30273 val_loss= 1.37721 val_acc= 0.26786 time= 0.01563
Epoch: 0027 train_loss= 1.37696 train_acc= 0.30273 val_loss= 1.37708 val_acc= 0.26786 time= 0.01563
Epoch: 0028 train_loss= 1.37749 train_acc= 0.30273 val_loss= 1.37686 val_acc= 0.26786 time= 0.01563
Epoch: 0029 train_loss= 1.37778 train_acc= 0.30273 val_loss= 1.37662 val_acc= 0.26786 time= 0.01563
Epoch: 0030 train_loss= 1.37748 train_acc= 0.30273 val_loss= 1.37638 val_acc= 0.26786 time= 0.01563
Epoch: 0031 train_loss= 1.37626 train_acc= 0.30273 val_loss= 1.37619 val_acc= 0.26786 time= 0.01563
Epoch: 0032 train_loss= 1.37691 train_acc= 0.30273 val_loss= 1.37604 val_acc= 0.26786 time= 0.01563
Epoch: 0033 train_loss= 1.37629 train_acc= 0.30273 val_loss= 1.37595 val_acc= 0.26786 time= 0.01563
Epoch: 0034 train_loss= 1.37621 train_acc= 0.30273 val_loss= 1.37593 val_acc= 0.26786 time= 0.01563
Epoch: 0035 train_loss= 1.37625 train_acc= 0.30273 val_loss= 1.37596 val_acc= 0.26786 time= 0.01562
Epoch: 0036 train_loss= 1.37696 train_acc= 0.30273 val_loss= 1.37602 val_acc= 0.26786 time= 0.01563
Epoch: 0037 train_loss= 1.37596 train_acc= 0.30273 val_loss= 1.37609 val_acc= 0.26786 time= 0.01563
Epoch: 0038 train_loss= 1.37645 train_acc= 0.30273 val_loss= 1.37614 val_acc= 0.26786 time= 0.01563
Epoch: 0039 train_loss= 1.37629 train_acc= 0.30273 val_loss= 1.37621 val_acc= 0.26786 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.37850 accuracy= 0.31858 time= 0.00000 
