Epoch: 0001 train_loss= 1.38813 train_acc= 0.26172 val_loss= 1.39942 val_acc= 0.21429 time= 0.54691
Epoch: 0002 train_loss= 1.38711 train_acc= 0.25195 val_loss= 1.39667 val_acc= 0.21429 time= 0.01563
Epoch: 0003 train_loss= 1.38668 train_acc= 0.27539 val_loss= 1.39410 val_acc= 0.21429 time= 0.00000
Epoch: 0004 train_loss= 1.38594 train_acc= 0.25781 val_loss= 1.39172 val_acc= 0.32143 time= 0.01563
Epoch: 0005 train_loss= 1.38583 train_acc= 0.24219 val_loss= 1.38962 val_acc= 0.32143 time= 0.00000
Epoch: 0006 train_loss= 1.38392 train_acc= 0.29688 val_loss= 1.38776 val_acc= 0.32143 time= 0.02067
Epoch: 0007 train_loss= 1.38346 train_acc= 0.30078 val_loss= 1.38615 val_acc= 0.32143 time= 0.01101
Epoch: 0008 train_loss= 1.38280 train_acc= 0.30078 val_loss= 1.38470 val_acc= 0.32143 time= 0.00000
Epoch: 0009 train_loss= 1.38252 train_acc= 0.30078 val_loss= 1.38353 val_acc= 0.32143 time= 0.01563
Epoch: 0010 train_loss= 1.38129 train_acc= 0.29883 val_loss= 1.38246 val_acc= 0.32143 time= 0.00000
Epoch: 0011 train_loss= 1.38225 train_acc= 0.29883 val_loss= 1.38150 val_acc= 0.32143 time= 0.01563
Epoch: 0012 train_loss= 1.38215 train_acc= 0.29883 val_loss= 1.38060 val_acc= 0.32143 time= 0.00000
Epoch: 0013 train_loss= 1.38123 train_acc= 0.29883 val_loss= 1.37990 val_acc= 0.32143 time= 0.00000
Epoch: 0014 train_loss= 1.38168 train_acc= 0.29883 val_loss= 1.37934 val_acc= 0.32143 time= 0.01563
Epoch: 0015 train_loss= 1.38069 train_acc= 0.29883 val_loss= 1.37882 val_acc= 0.32143 time= 0.00000
Epoch: 0016 train_loss= 1.38186 train_acc= 0.29883 val_loss= 1.37851 val_acc= 0.32143 time= 0.01563
Epoch: 0017 train_loss= 1.38181 train_acc= 0.29883 val_loss= 1.37822 val_acc= 0.32143 time= 0.00000
Epoch: 0018 train_loss= 1.38133 train_acc= 0.29883 val_loss= 1.37805 val_acc= 0.32143 time= 0.01563
Epoch: 0019 train_loss= 1.38213 train_acc= 0.29883 val_loss= 1.37790 val_acc= 0.32143 time= 0.00000
Epoch: 0020 train_loss= 1.38243 train_acc= 0.29883 val_loss= 1.37785 val_acc= 0.32143 time= 0.01563
Epoch: 0021 train_loss= 1.38091 train_acc= 0.29883 val_loss= 1.37800 val_acc= 0.32143 time= 0.00000
Epoch: 0022 train_loss= 1.38125 train_acc= 0.29883 val_loss= 1.37816 val_acc= 0.32143 time= 0.01563
Epoch: 0023 train_loss= 1.38043 train_acc= 0.29883 val_loss= 1.37834 val_acc= 0.32143 time= 0.00000
Epoch: 0024 train_loss= 1.38075 train_acc= 0.29883 val_loss= 1.37842 val_acc= 0.32143 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.35593 accuracy= 0.36283 time= 0.00000 
