Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
Table of Contents (17 chapters)
Title Page
About Packt

Relation networks

Now, we will see another interesting one-shot learning algorithm, called a relation network. It is one of the simplest and most efficient one-shot learning algorithms. We will explore how relation networks are used in one-shot, few-shot, and zero-shot learning settings.

Relation networks in one-shot learning

A relation network consists of two important functions: the embedding function, denoted by

, and the relation function, denoted by

. The embedding function is used for extracting the features from the input. If our input is an image, then we can use a convolutional network as our embedding function, which will give us the feature vectors/embeddings of an image. If our input is a text, then we can use LSTM networks to get the embeddings of the text.

As we know, in one-shot learning, we have only a single example per class. For example, let's say our support set contains three classes with one example per class. As shown in the following diagram, we have a support set containing...