# Can a neural network be of variable depth?

It is very common for neural networks to be asymmetric about the x axis, that is, to have many more nuerons in the first few layers than in the last few layers. Common example:

But can neural networks be structured in such a way that they're asymmetric about the y axis, that is, such that some parts of the network are 'deeper' than others?

An example of where this could be useful is where a problem involves some unsophisticated features (that require only a small number of layers), and some much more sophisticated features (which may benefit greatly from having more layers - i.e. greater 'depth').

### "Wide and Deep network" was used in a 2016 paper.

$$\hspace{5cm}$$Wide & Deep Learning for Recommender Systems

You may create using keras Concatenate layer

concat = keras.layers.Concatenate()([input, hidden2])