Epoch: 0001 train_loss= 1.39064 train_acc= 0.19544 val_loss= 1.39740 val_acc= 0.16071 time= 0.26549
Epoch: 0002 train_loss= 1.38981 train_acc= 0.19870 val_loss= 1.39618 val_acc= 0.16071 time= 0.01563
Epoch: 0003 train_loss= 1.38936 train_acc= 0.20847 val_loss= 1.39500 val_acc= 0.17857 time= 0.00000
Epoch: 0004 train_loss= 1.38707 train_acc= 0.25081 val_loss= 1.39372 val_acc= 0.28571 time= 0.01563
Epoch: 0005 train_loss= 1.38611 train_acc= 0.26059 val_loss= 1.39245 val_acc= 0.32143 time= 0.00000
Epoch: 0006 train_loss= 1.38495 train_acc= 0.32899 val_loss= 1.39123 val_acc= 0.32143 time= 0.01563
Epoch: 0007 train_loss= 1.38273 train_acc= 0.34202 val_loss= 1.38984 val_acc= 0.32143 time= 0.00000
Epoch: 0008 train_loss= 1.38211 train_acc= 0.34528 val_loss= 1.38837 val_acc= 0.32143 time= 0.00000
Epoch: 0009 train_loss= 1.38033 train_acc= 0.34202 val_loss= 1.38689 val_acc= 0.32143 time= 0.01563
Epoch: 0010 train_loss= 1.37972 train_acc= 0.34202 val_loss= 1.38534 val_acc= 0.32143 time= 0.00000
Epoch: 0011 train_loss= 1.37700 train_acc= 0.34202 val_loss= 1.38380 val_acc= 0.32143 time= 0.01563
Epoch: 0012 train_loss= 1.37684 train_acc= 0.34202 val_loss= 1.38225 val_acc= 0.32143 time= 0.00000
Epoch: 0013 train_loss= 1.37510 train_acc= 0.34202 val_loss= 1.38081 val_acc= 0.32143 time= 0.00000
Epoch: 0014 train_loss= 1.37513 train_acc= 0.34202 val_loss= 1.37944 val_acc= 0.32143 time= 0.01563
Epoch: 0015 train_loss= 1.37203 train_acc= 0.34202 val_loss= 1.37824 val_acc= 0.32143 time= 0.00000
Epoch: 0016 train_loss= 1.36987 train_acc= 0.34202 val_loss= 1.37712 val_acc= 0.32143 time= 0.01563
Epoch: 0017 train_loss= 1.37236 train_acc= 0.34202 val_loss= 1.37605 val_acc= 0.32143 time= 0.00000
Epoch: 0018 train_loss= 1.36946 train_acc= 0.34202 val_loss= 1.37497 val_acc= 0.32143 time= 0.00000
Epoch: 0019 train_loss= 1.36910 train_acc= 0.34202 val_loss= 1.37399 val_acc= 0.32143 time= 0.01563
Epoch: 0020 train_loss= 1.37030 train_acc= 0.34202 val_loss= 1.37300 val_acc= 0.32143 time= 0.00000
Epoch: 0021 train_loss= 1.36824 train_acc= 0.34202 val_loss= 1.37195 val_acc= 0.32143 time= 0.01563
Epoch: 0022 train_loss= 1.36885 train_acc= 0.34202 val_loss= 1.37094 val_acc= 0.32143 time= 0.00000
Epoch: 0023 train_loss= 1.36697 train_acc= 0.34202 val_loss= 1.36997 val_acc= 0.32143 time= 0.01562
Epoch: 0024 train_loss= 1.36564 train_acc= 0.34202 val_loss= 1.36909 val_acc= 0.32143 time= 0.00000
Epoch: 0025 train_loss= 1.36844 train_acc= 0.34202 val_loss= 1.36833 val_acc= 0.32143 time= 0.01563
Epoch: 0026 train_loss= 1.36609 train_acc= 0.34202 val_loss= 1.36767 val_acc= 0.32143 time= 0.00000
Epoch: 0027 train_loss= 1.36610 train_acc= 0.34202 val_loss= 1.36720 val_acc= 0.32143 time= 0.01563
Epoch: 0028 train_loss= 1.36626 train_acc= 0.34202 val_loss= 1.36694 val_acc= 0.32143 time= 0.00000
Epoch: 0029 train_loss= 1.36772 train_acc= 0.34202 val_loss= 1.36673 val_acc= 0.32143 time= 0.01563
Epoch: 0030 train_loss= 1.36880 train_acc= 0.34202 val_loss= 1.36669 val_acc= 0.32143 time= 0.00000
Epoch: 0031 train_loss= 1.36979 train_acc= 0.34202 val_loss= 1.36669 val_acc= 0.32143 time= 0.01563
Epoch: 0032 train_loss= 1.36818 train_acc= 0.34202 val_loss= 1.36673 val_acc= 0.32143 time= 0.00000
Epoch: 0033 train_loss= 1.36775 train_acc= 0.34202 val_loss= 1.36676 val_acc= 0.32143 time= 0.01563
Epoch: 0034 train_loss= 1.36850 train_acc= 0.34202 val_loss= 1.36687 val_acc= 0.32143 time= 0.00000
Epoch: 0035 train_loss= 1.36627 train_acc= 0.34202 val_loss= 1.36689 val_acc= 0.32143 time= 0.00000
Epoch: 0036 train_loss= 1.36708 train_acc= 0.34202 val_loss= 1.36693 val_acc= 0.32143 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.37329 accuracy= 0.30973 time= 0.00000 
