This article was written by Jason Brownlee.
Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is via systematic experimentation with a robust test harness. This can be a tough pill to swallow for beginners to the field of machine learning, looking for an analytical way to calculate the optimal number of layers and nodes, or easy rules of thumb to follow.
In this post, you will discover the roles of layers and nodes and how to approach the configuration of a multilayer perceptron neural network for your predictive modeling problem. After reading this post, you will know:
Let’s get started.
This post is divided into four sections; they are:
The Multilayer Perceptron
A node, also called a neuron or Perceptron, is a computational unit that has one or more weighted input connections, a transfer function that combines the inputs in some way, and an output connection. Nodes are then organized into layers to comprise a network.
A single-layer artificial neural network, also called a single-layer, has a single layer of nodes, as its name suggests. Each node in the single layer connects directly to an input variable and contributes to an output variable. A single-layer network can be extended to a multiple-layer network, referred to as a Multilayer Perceptron. A Multilayer Perceptron, or MLP for sort, is an artificial neural network with more than a single layer.
It has an input layer that connects to the input variables, one or more hidden layers, and an output layer that produces the output variables. We can summarize the types of layers in an MLP as follows:
Finally, there are terms used to describe the shape and capability of a neural network; for example: