Epoch: 0001 train_loss= 1.39475 train_acc= 0.18994 val_loss= 1.39004 val_acc= 0.25000 time= 0.32853
Epoch: 0002 train_loss= 1.39220 train_acc= 0.24302 val_loss= 1.38687 val_acc= 0.25000 time= 0.01525
Epoch: 0003 train_loss= 1.39025 train_acc= 0.24302 val_loss= 1.38478 val_acc= 0.39286 time= 0.01563
Epoch: 0004 train_loss= 1.38867 train_acc= 0.31425 val_loss= 1.38298 val_acc= 0.39286 time= 0.01563
Epoch: 0005 train_loss= 1.38764 train_acc= 0.31285 val_loss= 1.38124 val_acc= 0.39286 time= 0.01563
Epoch: 0006 train_loss= 1.38678 train_acc= 0.31285 val_loss= 1.37950 val_acc= 0.39286 time= 0.01563
Epoch: 0007 train_loss= 1.38582 train_acc= 0.31285 val_loss= 1.37780 val_acc= 0.39286 time= 0.01563
Epoch: 0008 train_loss= 1.38486 train_acc= 0.31285 val_loss= 1.37615 val_acc= 0.39286 time= 0.01563
Epoch: 0009 train_loss= 1.38373 train_acc= 0.31285 val_loss= 1.37457 val_acc= 0.39286 time= 0.01563
Epoch: 0010 train_loss= 1.38326 train_acc= 0.31285 val_loss= 1.37304 val_acc= 0.39286 time= 0.01563
Epoch: 0011 train_loss= 1.38202 train_acc= 0.31285 val_loss= 1.37161 val_acc= 0.39286 time= 0.01563
Epoch: 0012 train_loss= 1.38141 train_acc= 0.31285 val_loss= 1.37029 val_acc= 0.39286 time= 0.01563
Epoch: 0013 train_loss= 1.38046 train_acc= 0.31285 val_loss= 1.36906 val_acc= 0.39286 time= 0.01563
Epoch: 0014 train_loss= 1.37964 train_acc= 0.31285 val_loss= 1.36798 val_acc= 0.39286 time= 0.01563
Epoch: 0015 train_loss= 1.37885 train_acc= 0.31285 val_loss= 1.36706 val_acc= 0.39286 time= 0.01563
Epoch: 0016 train_loss= 1.37801 train_acc= 0.31285 val_loss= 1.36633 val_acc= 0.39286 time= 0.03125
Epoch: 0017 train_loss= 1.37738 train_acc= 0.31285 val_loss= 1.36580 val_acc= 0.39286 time= 0.01562
Epoch: 0018 train_loss= 1.37738 train_acc= 0.31285 val_loss= 1.36546 val_acc= 0.39286 time= 0.01563
Epoch: 0019 train_loss= 1.37680 train_acc= 0.31285 val_loss= 1.36533 val_acc= 0.39286 time= 0.01563
Epoch: 0020 train_loss= 1.37656 train_acc= 0.31285 val_loss= 1.36540 val_acc= 0.39286 time= 0.01563
Epoch: 0021 train_loss= 1.37624 train_acc= 0.31285 val_loss= 1.36568 val_acc= 0.39286 time= 0.01563
Epoch: 0022 train_loss= 1.37591 train_acc= 0.31285 val_loss= 1.36611 val_acc= 0.39286 time= 0.01563
Epoch: 0023 train_loss= 1.37543 train_acc= 0.31285 val_loss= 1.36666 val_acc= 0.39286 time= 0.01563
Early stopping...
Optimization Finished!
Test set results: cost= 1.38524 accuracy= 0.31858 time= 0.00000 
