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

When you work with neural networks in artificial intelligence (AI) and machine learning, you can use normalization techniques to accelerate and improve generalization, including layer and batch normalization. Generalization refers to the model's ability to process previously unseen data. Neural networks process data similarly to how the human brain processes information. As a type of machine learning, this method helps a computer to learn from mistakes, allowing it to improve over time. 

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(35,897 ratings)

698,281 already enrolled

Beginner level

Learn More

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Classification And Regression Tree (CART), Feature Engineering, Tensorflow, Artificial Intelligence, Jupyter, Machine Learning, Predictive Modeling, Reinforcement Learning, Scikit Learn (Machine Learning Library), Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), NumPy, Unsupervised Learning, Supervised Learning, Data Ethics, Random Forest Algorithm, Decision Tree Learning, Applied Machine Learning, Python Programming, Responsible AI, Regression Analysis, Statistical Modeling, Data Transformation, Anomaly Detection, Algorithms, Dimensionality Reduction, Artificial Neural Networks, Performance Tuning

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. 

To utilize neural networks, you must train the network by using algorithms and having the network process large datasets. Some neural networks contain many hidden layers, making it increasingly difficult to train in a timely manner with additional layers involved. This issue becomes more complicated with more layers since the hidden layers in a network receive input from the previous layer before passing it on. This causes the process to slow down and requires more careful parameters to be put into place. Layer and batch normalization are popular techniques used to accelerate the training process.

Explore the difference between the two methods and why each one is useful.

What is batch normalization?

Batch normalization allows for higher learning rates by using fewer training steps and normalization training in mini-batches. Sergey Ioffe and Christian Szegedy at Google first introduced the method 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.

What is batch normalization used for?

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 of batch normalization

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 of batch normalization

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.

What is layer normalization?

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]. 

What is layer normalization used for?

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 of layer normalization

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 of layer normalization

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.

Other things to consider

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

Getting started with Coursera

If you want to start working with layer or batch normalization techniques, you should consider learning the basics of machine learning and AI. 

You can explore machine learning concepts, including how to build and train a neural network, through the Machine Learning Specialization offered by Stanford and DeepLearning.AI on Coursera.

If you’re seeking a professional certificate to prepare for AI engineer roles, you can check out IBM’s AI Engineering Professional Certificate on Coursera. This 13-course series involves learning machine learning algorithms, deep learning and neural network models, and more.

specialization

Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

4.9

(35,897 ratings)

698,281 already enrolled

Beginner level

Learn More

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Classification And Regression Tree (CART), Feature Engineering, Tensorflow, Artificial Intelligence, Jupyter, Machine Learning, Predictive Modeling, Reinforcement Learning, Scikit Learn (Machine Learning Library), Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), NumPy, Unsupervised Learning, Supervised Learning, Data Ethics, Random Forest Algorithm, Decision Tree Learning, Applied Machine Learning, Python Programming, Responsible AI, Regression Analysis, Statistical Modeling, Data Transformation, Anomaly Detection, Algorithms, Dimensionality Reduction, Artificial Neural Networks, Performance Tuning

professional certificate

IBM AI Engineering

Get job-ready as an AI engineer . Build the AI engineering skills and practical experience you need to catch the eye of an employer in less than 4 months. Power up your resume!

4.5

(7,932 ratings)

167,318 already enrolled

Intermediate level

Learn More

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

PyTorch (Machine Learning Library), Feature Engineering, Artificial Neural Networks, Data Science, Large Language Modeling, Tensorflow, LLM Application, Machine Learning, Machine Learning Methods, Natural Language Processing, Generative AI Agents, Reinforcement Learning, Generative AI, Deep Learning, Unsupervised Learning, Keras (Neural Network Library), Supervised Learning, Applied Machine Learning, Python Programming, Prompt Engineering, Computer Vision, Artificial Intelligence, Regression Analysis, Dimensionality Reduction, Scikit Learn (Machine Learning Library), Classification And Regression Tree (CART), Decision Tree Learning, Statistical Modeling, Predictive Modeling, Network Architecture, Network Model, Image Analysis, User Interface (UI), Database Management Systems, Data Storage Technologies, Document Management, Probability & Statistics, Data Manipulation, Data Ethics, Statistical Methods, Text Mining, Data Processing, Data Pipelines, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML)