function createDenseModel() {
  const model = tf.sequential();
  model.add(tf.layers.flatten({inputShape: [IMAGE_H, IMAGE_W, 1]}));
  model.add(tf.layers.dense({units: 42, activation: 'relu'}));
  model.add(tf.layers.dense({units: 10, activation: 'softmax'}));
  return model;
}
Creates a model consisting of only flatten, dense and dropout layers.

The model create here has approximately the same number of parameters
(~31k) as the convnet created by `createConvModel()`, but is
expected to show a significantly worse accuracy after training, due to the
fact that it doesn't utilize the spatial information as the convnet does.

This is for comparison with the convolutional network above.

@returns {tf.Model} An instance of tf.Model.