Epoch: 0001 train_loss= 1.40207 train_acc= 0.24023 val_loss= 1.39942 val_acc= 0.21429 time= 0.53177
Epoch: 0002 train_loss= 1.39521 train_acc= 0.24219 val_loss= 1.39358 val_acc= 0.21429 time= 0.00000
Epoch: 0003 train_loss= 1.38943 train_acc= 0.24805 val_loss= 1.38838 val_acc= 0.21429 time= 0.01563
Epoch: 0004 train_loss= 1.38784 train_acc= 0.24023 val_loss= 1.38386 val_acc= 0.25000 time= 0.00000
Epoch: 0005 train_loss= 1.38238 train_acc= 0.27734 val_loss= 1.38005 val_acc= 0.33929 time= 0.01563
Epoch: 0006 train_loss= 1.37955 train_acc= 0.32422 val_loss= 1.37692 val_acc= 0.33929 time= 0.00000
Epoch: 0007 train_loss= 1.37727 train_acc= 0.33203 val_loss= 1.37444 val_acc= 0.33929 time= 0.01563
Epoch: 0008 train_loss= 1.37668 train_acc= 0.33008 val_loss= 1.37251 val_acc= 0.33929 time= 0.00000
Epoch: 0009 train_loss= 1.37623 train_acc= 0.33008 val_loss= 1.37102 val_acc= 0.33929 time= 0.01563
Epoch: 0010 train_loss= 1.37379 train_acc= 0.33008 val_loss= 1.36989 val_acc= 0.33929 time= 0.00000
Epoch: 0011 train_loss= 1.37379 train_acc= 0.33008 val_loss= 1.36904 val_acc= 0.33929 time= 0.00000
Epoch: 0012 train_loss= 1.37432 train_acc= 0.33008 val_loss= 1.36837 val_acc= 0.33929 time= 0.01563
Epoch: 0013 train_loss= 1.37438 train_acc= 0.33008 val_loss= 1.36780 val_acc= 0.33929 time= 0.00000
Epoch: 0014 train_loss= 1.37565 train_acc= 0.33008 val_loss= 1.36728 val_acc= 0.33929 time= 0.01563
Epoch: 0015 train_loss= 1.37359 train_acc= 0.33008 val_loss= 1.36683 val_acc= 0.33929 time= 0.00000
Epoch: 0016 train_loss= 1.37375 train_acc= 0.33008 val_loss= 1.36645 val_acc= 0.33929 time= 0.01562
Epoch: 0017 train_loss= 1.37357 train_acc= 0.33008 val_loss= 1.36617 val_acc= 0.33929 time= 0.00000
Epoch: 0018 train_loss= 1.37375 train_acc= 0.33008 val_loss= 1.36599 val_acc= 0.33929 time= 0.00000
Epoch: 0019 train_loss= 1.37202 train_acc= 0.33008 val_loss= 1.36592 val_acc= 0.33929 time= 0.01563
Epoch: 0020 train_loss= 1.37296 train_acc= 0.33008 val_loss= 1.36596 val_acc= 0.33929 time= 0.00000
Epoch: 0021 train_loss= 1.37254 train_acc= 0.33008 val_loss= 1.36609 val_acc= 0.33929 time= 0.01563
Epoch: 0022 train_loss= 1.37416 train_acc= 0.33008 val_loss= 1.36630 val_acc= 0.33929 time= 0.00000
Epoch: 0023 train_loss= 1.37161 train_acc= 0.33008 val_loss= 1.36654 val_acc= 0.33929 time= 0.01562
Early stopping...
Optimization Finished!
Test set results: cost= 1.35155 accuracy= 0.36283 time= 0.00000 
