Epoch: 0001 train_loss= 1.39329 train_acc= 0.18893 val_loss= 1.39355 val_acc= 0.19643 time= 0.12501
Epoch: 0002 train_loss= 1.39205 train_acc= 0.18893 val_loss= 1.39217 val_acc= 0.30357 time= 0.00000
Epoch: 0003 train_loss= 1.39073 train_acc= 0.33225 val_loss= 1.39072 val_acc= 0.30357 time= 0.01563
Epoch: 0004 train_loss= 1.38907 train_acc= 0.33225 val_loss= 1.38922 val_acc= 0.30357 time= 0.01563
Epoch: 0005 train_loss= 1.38751 train_acc= 0.33225 val_loss= 1.38763 val_acc= 0.30357 time= 0.03125
Epoch: 0006 train_loss= 1.38621 train_acc= 0.33225 val_loss= 1.38600 val_acc= 0.30357 time= 0.01563
Epoch: 0007 train_loss= 1.38347 train_acc= 0.33225 val_loss= 1.38474 val_acc= 0.30357 time= 0.01563
Epoch: 0008 train_loss= 1.38238 train_acc= 0.33225 val_loss= 1.38350 val_acc= 0.30357 time= 0.01563
Epoch: 0009 train_loss= 1.38069 train_acc= 0.33225 val_loss= 1.38225 val_acc= 0.30357 time= 0.01563
Epoch: 0010 train_loss= 1.37968 train_acc= 0.33225 val_loss= 1.38118 val_acc= 0.30357 time= 0.01563
Epoch: 0011 train_loss= 1.37838 train_acc= 0.33225 val_loss= 1.38020 val_acc= 0.30357 time= 0.01563
Epoch: 0012 train_loss= 1.37593 train_acc= 0.33225 val_loss= 1.37935 val_acc= 0.30357 time= 0.01563
Epoch: 0013 train_loss= 1.37519 train_acc= 0.33225 val_loss= 1.37857 val_acc= 0.30357 time= 0.01563
Epoch: 0014 train_loss= 1.37428 train_acc= 0.33225 val_loss= 1.37785 val_acc= 0.30357 time= 0.01563
Epoch: 0015 train_loss= 1.37277 train_acc= 0.33225 val_loss= 1.37741 val_acc= 0.30357 time= 0.01563
Epoch: 0016 train_loss= 1.37108 train_acc= 0.33225 val_loss= 1.37711 val_acc= 0.30357 time= 0.01563
Epoch: 0017 train_loss= 1.36967 train_acc= 0.33225 val_loss= 1.37695 val_acc= 0.30357 time= 0.01563
Epoch: 0018 train_loss= 1.36821 train_acc= 0.33225 val_loss= 1.37691 val_acc= 0.30357 time= 0.01563
Epoch: 0019 train_loss= 1.36804 train_acc= 0.33225 val_loss= 1.37681 val_acc= 0.30357 time= 0.01563
Epoch: 0020 train_loss= 1.36785 train_acc= 0.33225 val_loss= 1.37680 val_acc= 0.30357 time= 0.01563
Epoch: 0021 train_loss= 1.36877 train_acc= 0.33225 val_loss= 1.37697 val_acc= 0.30357 time= 0.01563
Epoch: 0022 train_loss= 1.36839 train_acc= 0.33225 val_loss= 1.37727 val_acc= 0.30357 time= 0.01562
Epoch: 0023 train_loss= 1.36847 train_acc= 0.33225 val_loss= 1.37772 val_acc= 0.30357 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38979 accuracy= 0.29204 time= 0.00000 
