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.
- Size: The number of nodes in the model.
- Width: The number of nodes in a specific layer.
- Depth: The number of layers in a neural network.
- Capacity: The type or structure of functions that can be learned by a network configuration. Sometimes called “representational capacity“.
- Architecture: The specific arrangement of the layers and nodes in the network.
Traditionally, there is some disagreement about how to count the number of layers.
The disagreement centers around whether or not the input layer is counted. There is an argument to suggest it should not be counted because the inputs are not active; they are simply the input variables.
How many layers should you use in your Multilayer Perceptron and how many nodes per layer?
In this section, we will enumerate five approaches to solving this problem.
1) Experimentation; 2) Intuition; 3) Go For Depth; 4) Borrow Ideas;
5) Search
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