Layer Normalization vs. Batch Normalization: What’s the Difference?

When you work with neural networks in artificial intelligence (AI) and machine learning, techniques like batch and layer normalization can help you accelerate and improve generalization. At a glance, here's what you need to know about these two normalization processes:

• Batch normalization splits large data sets in smaller batches, adjusting from one batch to another.

• Layer normalization applies the same computation to one sequence at a time.

• Both of these processes can help a neural network process data efficiently; though, they each come with a set of advantages and disadvantages.

Below, you'll learn more about batch normalization vs layer normalization. Afterward, if you want to develop your deep learning abilities, consider enrolling in DeepLearning.AI's Deep Learning Specialization.

What is data normalization in machine learning?

Data normalization is the process of resizing feature values to a particularly scale, often between 0 and 1, to ensure data is scaled into a standard range. Because neural networks contain datasets with numeric inputs of different ranges and scales, normalizing these values helps prevent slow training times and instability.

Neural networks contain many layers of artificial neurons, which are software programs or algorithms that solve calculations. A simple neural network also has three basic layers: an input layer, a hidden layer, and an output layer. Some neural networks contain many hidden layers, making it increasingly difficult to train in a timely manner with additional layers involved. This causes the process to slow down and requires more careful parameters to be put into place.

Layer and batch normalization are popular regularization techniques used to accelerate the training process. Below, you'll explore the difference between the two methods and why each one is useful.

Batch normalization explained

Batch normalization allows for higher learning rates by using fewer training steps and normalization training in mini-batches. The technique was first introduce by Sergey Ioffe and Christian Szegedy at Google in 2015 [1]. By splitting large datasets into mini-batches, the algorithm can optimize specific neural network parameters through each smaller batch while updating and adjusting each unit’s mean and variance along the way.

Uses

Various learning models use batch normalization, including Convolutional Neural Networks (CNNs). Other learning models include the following:

• Deep Neural Networks (DNNs)

• Multilayer Perceptrons

• Recurrent Neural Networks (RNNs)

Advantages

Batch normalization has a few benefits, including the following:

• Stabilizes the training process: Since this method provides additional stability throughout the training process, neural networks can learn more information.

• Improves the neural network’s generalization performance: This method reduces the need for other regularization methods by improving performance.

• Improves learning speed: Batch normalization accelerates convergence speeds, normalizes activations, and prevents small parameter changes throughout training.

Disadvantages

While batch normalization has benefits in improving the speed and stability of neural network training, the method has a few drawbacks as well, including the following:

• Not suitable for smaller batch sizes: The sample mean and standard deviation used for small batches do not properly represent the actual distribution for the network.

• Mini-batch statistics can lead to inconsistencies: Mini-batch statistics are not available within inference settings, so performance may become limited in these instances.

• Not suitable for sequence models: While batch normalization can apply to recurrent networks, such as RNNs, it does not perform well on varying sequence lengths.

Layer normalization explained

Layer normalization has the same distributions across all features in each layer. While batch normalization computes and adjusts the mean and variance over each mini-batch, layer normalization performs the same computation that applies to one observation at a time. Layer normalization was introduced shortly after batch normalization in 2016 [2]. 

Uses

Since layer normalization does not rely on batches, it is especially useful when working with sequence models, especially RNNs. Since RNNs have various batch sizes, layer normalization can benefit training these networks. Layer normalization also works well with this architecture as it has long-range dependencies.

Advantages

Layer normalization has many benefits, including the following:

• Enhanced performance: Generalizes data and unseen data, which leads to improved performance and smoother gradients.

• Adaptable to various batch sizes: Suitable for smaller or more variable batch sizes.

• Stabilized training: The method provides more flexibility when input and batch data changes.

Disadvantages

Compared to batch normalization, the main disadvantage of using layer normalization relates to batch size. While layer normalization doesn’t depend on batch size for performance, it may not perform optimally for large batches. If you used layer normalization for larger batches, it could lead to slower processing and training times.

When to use layer normalization vs. batch normalization

Batch and layer normalization both give users the power to stabilize and improve the speed when training neural networks.

The method you choose depends on various factors and uses. You may consider the size of the data sets you’re working with, the neural network’s architecture, and any computational constraints. For example, batch normalization involves additional computational overhead. This means if your hardware or systems have limited resources, the training will require more operations for processing mini-batches. In this case, you may want to consider other frameworks or workarounds for training.

Some of the most common real-world applications for normalization techniques include the following:

- Image processing

- Natural Language Processing

- Generative modeling

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