Relationship Between Number of Layers and Model Performance
The number of layers in a neural network has a significant impact on the model's performance.
If there are too few layers, learning may be insufficient, while too many layers can lead to overfitting.
In this lesson, we will examine how the number of layers affects the performance of a neural network.
1. When There Are Too Few Layers: Underfitting
If there are too few layers, the neural network fails to learn enough patterns, resulting in poor performance.
In such cases, while it may handle simple problems, it struggles with learning complex data patterns.
Both training and test data may show low performance, indicating the model's inability to generalize.
For example, consider the following neural network structure.
Input Layer → Hidden Layer (10 Neurons) → Output Layer
This structure has few neurons and is shallow, limiting its ability to learn complex data patterns.
2. When There Are Too Many Layers: Overfitting
Having too many layers can cause the neural network to fit excessively to the training data, reducing generalization performance.
In this scenario, the model may perform well on training data but poorly on new data.
Additionally, increased computational cost may slow down the training process, introducing unnecessary complexity.
For instance, the structure below with an excessive number of hidden layers may lead to unnecessary complexity.
Input Layer → Hidden Layer (256 Neurons) → Hidden Layer (128 Neurons) → Hidden Layer (64 Neurons) → Output Layer
Such a model may optimize well on training data but may not perform well on new data.
Using an appropriate number of layers ensures balanced performance on both training and test data, allowing adequate learning without overfitting.
To find the optimal number of layers, it is advisable to start with a smaller model and gradually add layers while monitoring performance changes.
Moreover, employing a validation dataset can help evaluate the model's generalization performance and adjust the number of layers accordingly.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.