What is layer normalization?

by Stephen M. Walker II, Co-Founder / CEO

What is layer normalization?

Layer normalization (LayerNorm) is a technique used in deep learning to normalize the distributions of intermediate layers. It was proposed by researchers Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. The primary goal of layer normalization is to stabilize the learning process and accelerate the training of deep neural networks.

Unlike batch normalization, which normalizes the input across the batch dimension, layer normalization performs normalization for each feature across the feature dimension. This means that in layer normalization, all neurons in a particular layer effectively have the same distribution across all features for a given input.

Mathematically, layer normalization computes the mean and variance for each feature across the feature dimension.

One of the key advantages of layer normalization is that it performs the same computation at training and test times, and it does not impose any constraint on the size of the mini-batch. This makes it particularly suitable for sequence models such as transformers and recurrent neural networks, where the sequence length can vary.

In the context of convolutional layers, layer normalization is applied channel-wise, treating each channel as an independent sample.

Overall, layer normalization enables smoother gradients, faster training, and better generalization accuracy.

How does layer normalization differ from batch normalization?

Layer normalization and batch normalization are both techniques used in deep learning to normalize the distributions of intermediate layers, but they differ in several key aspects:

  1. Normalization Dimension — Batch normalization normalizes the input across the batch dimension, meaning for each feature, it calculates the mean and variance across all instances in the batch. On the other hand, layer normalization operates over the feature dimension, calculating the mean and variance for each instance separately, over all the features.

  2. Batch Size Dependency — Batch normalization is dependent on batch size and requires larger batch sizes for effective approximation of the population statistics. This can cause issues in certain scenarios, such as small batch sizes or sequence models, where the batch size changes every time step. Layer normalization, however, is independent of the batch size, making it suitable for models with varying batch sizes.

  3. Training and Inference Processing — Batch normalization requires different processing at training and inference times. During training, it calculates the batch statistics (mean and variance), and during testing, a running average of these calculated during training is used. Layer normalization, in contrast, performs the same computation at training and test times.

  4. Application — Batch normalization is widely used in Convolutional Neural Networks (CNNs) as it can accelerate training and improve generalization. Layer normalization, however, is often used in recurrent models and transformers where batch normalization performs poorly due to varying sequence lengths.

  5. Normalization Statistics — In batch normalization, each input in the current mini-batch is transformed by subtracting the input mean in the batch and dividing by the standard deviation. In layer normalization, all the hidden units in a layer share the same normalization terms, but different training cases have different normalization terms.

More terms

What is neural machine translation?

Neural Machine Translation (NMT) is a state-of-the-art machine translation approach that uses artificial neural network techniques to predict the likelihood of a sequence of words. This can be a text fragment, a complete sentence, or even an entire document with the latest advances. NMT is a form of end-to-end learning that can be used to automatically produce translations.

Read more

What is Word2Vec?

Word2Vec is a technique in natural language processing (NLP) that provides vector representations of words. These vectors capture the semantic and syntactic qualities of words, and their usage in context. The Word2Vec algorithm estimates these representations by modeling text in a large corpus.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free