I would like to show an example of my model when it is overfitting, and when it is underfitting. Now overfitting is pretty straight forward, just train on small data, and the model will remember the data. But how do I show example of underfitting? I have a couple of suggestions:

1. lowering the learning rate, but I do not think this is really underfitting. This is just slow learn model.
2. add a dropout layer with 0.99, so that the network could not learn well enough.
3. maybe change the architecture somehow that it will be harder to learn.

I think (2) is the best option, what do you guys think?

My model:

```
conv2d (kernel=5,feature_maps=20)
batch_normalization
ReLU
Flatten()
FullyConnected(11,500->10)
Softmax

```

and the task it to label MNIST hand written.