A Practical Guide to Meta-Learning in Python

 A Practical Guide to Meta-Learning in Python explains the basics of meta-learning and helps

you understand the concept of meta-learning. You will experience a variety of one-time learning algorithms, such as concatenation

and memory augmentation networks, and implement them in TensorFlow and Keras.

TensorFlow and Keras. You will also learn about the latest meta-learning algorithms, such as model-free

related meta-learning (MAML), Reptile, and fast contextual adaptation through meta-learning (CAML).

(CAML). Then, you will explore how to use meta SGD for fast learning, and discover how to use meta learning for context-free learning.

and discover how to use meta-learning for unsupervised learning.

Who this book is for

This book will help machine learning enthusiasts who want to learn meta-learning as an advanced way to train machine learning models, artificial intelligence

machine learning enthusiasts, artificial intelligence researchers, and data scientists who want to learn meta-learning as an advanced method for training machine learning models. This book

book assumes that you have a practical knowledge of machine learning concepts and a thorough understanding of Python programming.

This book assumes that you have a practical knowledge of machine learning concepts and a thorough understanding of Python programming.

Meta-learning is one of the most promising and trending research areas in artificial intelligence today.

One of the most promising and trending research areas in AI. It is considered as a stepping stone to obtain generalized artificial intelligence (AGI). In this chapter

we will learn what meta-learning is and why it is the most exciting research in current artificial

intelligence and why meta-learning is the most exciting research in current AI. We will learn what is less-beat, single-beat, and

zero-beat learning, and how to use it in meta-learning. We will also learn about different

types of meta-learning techniques. Then, we will explore the concept of learning to learn by gradient descent

concept of gradient descent, where we learn how to use a meta-learner to learn

gradient descent optimization. Continuing, we will also learn optimization as several times learning

model, and we will learn how to use the meta-learner as an optimization algorithm in several learning settings.

optimization algorithm in several learning settings.

In this chapter, you will learn about the following.

meta-learning

Meta-learning and learning several times

Types of meta-learning

Meta-learning gradient descent by gradient descent

Optimizing models for several times learning

Meta-learning

Currently, meta-learning is an exciting area of research in AI. With a large number of

research papers and advances, it is clear that meta-learning has made significant breakthroughs in the AI field. Before

before we get into meta-learning, let's take a look at how our current AI models work

The

In recent years, with powerful algorithms such as generative adversarial networks and capsule networks, deep

Deep learning has grown by leaps and bounds in recent years with powerful algorithms such as generative adversarial networks and capsule networks. But the problem with deep neural networks is that

We need to have a large training set to train our models, and when we only have a small number of data points, it can be difficult to use.

When we have very few data points, it will suddenly fail. Suppose we have trained a deep

learning model to perform task A. Now, when we have a new task B, which is closely related to A

that is closely related to A, we cannot use the same model. We need to start from scratch

to train the model for task B. Thus, for each task, we need to start from scratch

for each task, even though they may be related.

Is deep learning really real AI? Well, no. How do humans learn

Learning? We generalize learning to multiple concepts and learn from them. But current

learning algorithms are only capable of performing one task. This is where meta-learning comes into play. Meta

learning produces a general AI model that can learn to perform a variety of tasks

without having to train from scratch. We use a small number of data points to train our meta

We use few data points to train our meta-learning model on various related tasks, so that for new related tasks, it can leverage data from previous tasks.

tasks, it can leverage the learning from previous tasks without having to train from scratch.

It does not have to start training from scratch. Many researchers and scientists believe that meta-learning can bring us closer to achieving AGI.

In the next section, we will learn exactly

meta-learning model how the meta-learning process works.

Meta-learning and Few Samples

Learning from fewer data points is called less-sample learning or k-time learning, where k

denotes the number of data points in each class in the dataset. Suppose we are classifying cats and

dog for image classification. If we have exactly one image of a dog and one image of a cat.

then it is called single learning, i.e., we learn from only one data point per class.

that is, we start learning from only one data point per class. If we have 10 images of dogs and 10 images of cats, it is called 10 times learning.

images, then it is called 10 times learning. Therefore, k in K-learning means that each class

has many data points. There is also zero learning, where each class does not have any data points.

Wait. What? How can we learn when there are no data points at all? In this case

case, we will have no data points, but will get meta-information about each class, and will learn from the meta-information.

and will learn from the meta-information. Since we have two classes in our dataset.

i.e., dogs and cats, we can refer to this as two-way learning k times learning; thus n ways

represents the number of classes we have in the dataset.

In order for our model to learn from some data points, we will train it in the same

way to train it. Thus, when we have a dataset D, we sample from each class present in the dataset.

data points from each class present in the dataset and call it the support

set. Similarly, we sample some different data points from each class and call them

are called query sets. Thus, we train the model using the support set and test it using the query

set for testing. We train the model in the form of episodes-that is, in each episode

episode, we sample some data points from dataset D, prepare the support set and the query set, and then use the support set in the query set.

then train on the support set and test on the query set. Thus

over a series of episodes, our model will learn how to learn from smaller datasets

learning. We will explore this in more detail in the next sections.

Types of meta-learning
From finding the best set of weights to learning optimizers, meta-learning can be done in a variety of ways
learning can be classified. We classify meta-learning into the following three categories.
Learning metric spaces
Learning initialization
Learning optimizers
Learning metric spaces
In the metric-based meta-learning setup, we will learn the appropriate metric space.
Suppose we want to learn the similarity between two images. In the metric-based setup
we use a simple neural network to extract features from the two images.
and find the similarity by computing the distance between the features of these two images. This
approach is widely used in several learning setups where we do not have many data points. In the
next section, we will learn metric-based learning algorithms such as concatenation
networks, prototype networks, and relational networks.
Learning initialization
In this approach, we try to learn the best initial parameter values. What does that
mean? Suppose we are building a neural network to classify images.
First, we initialize the random weights, calculate the loss, and minimize the loss by gradient descent.
loss is minimized by gradient descent. So, we will find the best weights by gradient descent and minimize the
minimizes the loss. Instead of initializing the weights randomly, if we can use the best
or close to the optimal value to initialize the weights, then we can reach
convergence and can learn quickly. In the next sections, we will see
how to find exactly these optimal initial weights using algorithms such as MAML, Reptile and meta SGD.
optimal initial weights.
Learning Optimizers
In this approach, we try to learn the optimizer. How do we usually optimize a neural
warp networks? We optimize neural networks by training them on large data sets and using gradient descent to minimize losses.
and using gradient descent to minimize losses. But in a sparse mathematical
learning setup, gradient descent fails because we will have a smaller dataset.
Therefore, in this case, we will learn the optimizer itself. We will have two
networks: a base network that actually tries to learn and a meta-network that optimizes that base network.
meta-network that optimizes that base network. In the next section, we will explore how this works.
Meta-learning gradient descent by gradient descent
Now, we will see an interesting meta-learning algorithm called "meta-learning gradient descent by gradient descent".
gradient descent to meta-learning gradient descent". Isn't that a daunting name? Well
Well, in fact, it is one of the simplest meta-learning algorithms. We know that in meta
learning, our goal is the meta-learning process. Usually, how do we train a neural
neural network? We train our network by calculating the loss and minimizing it through gradient descent
to train our network. Thus, we use gradient descent to optimize the model.
In addition to using gradient descent, can we learn this optimization process automatically?
But how do we learn it? We use recurrent neural networks (RNN) instead of
traditional optimizer (gradient descent). But how does this work? How can we use RNN instead of
instead of gradient descent? If you look closely, what exactly are we doing in gradient descent?
What is it? This is basically a series of updates from the output layer to the input layer, and we store
These updates are stored in a state. So, we can use an RNN and
store the updates in RNN cells.
So the main idea of the algorithm is to use RNN instead of gradient descent. But the question is
The problem is how does the RNN learn? How do we optimize the RNN? To optimize the
RNN, we use gradient descent. So, in short, we are learning
gradient descent through the RNN, and the RNN is optimized by gradient descent.
optimization, which is the name for learning gradient descent through gradient descent.
We call our RNN an optimizer, and we refer to our underlying network as an optimizer
optimizer. Suppose we have a model f parameterized by some parameter θ. We need to
to find the optimal parameter θ to minimize the loss. Usually, we find the optimal parameter by gradient down
but now we use RNN to find the optimal parameters.
Thus, the RNN (optimizer) finds the optimal parameters and sends them to the optimal (base
The optimizer uses this parameter, calculates the loss, and then sends the loss to the RNN.
Based on the loss, the RNN optimizes by gradient descent and updates the model
parameter θ.
Confused? Check out the following figure: Optimize (RNN) by Optimizer
Optimize (base network). The optimizer sends the updated parameters (i.e., weights)
to the optimizer, which uses these weights, calculates the loss, and then sends the loss
to the optimizer. Based on the losses, the optimizer improves itself by gradient descent.
Suppose our base network (optimizer) is parameterized by θ and our RNN (optimizer) is parameterized by φ.
(optimizer) is parametrized by φ. What is the loss function of the optimizer? We know what the optimizer
The role of the optimizer (RNN) is to reduce the loss of the optimizer (base network). Therefore, the
the loss of the optimizer is the average loss of the optimizer, which can be expressed as
How do we minimize this loss? By finding the correct φ, we can minimize the loss by Over-gradient descent minimizes this loss. Okay, what is the RNN as an input and what output will it return?

What is it, and what output will it return? Our optimizer, our RNN, takes the

the gradient of optimizer [t] and its previous state h[t] as input and returns the output

output, i.e., an update to g[t], which minimizes the loss of the optimizer. Let us

We denote our RNN by the function m.

v [t] is the gradient of our model (optimization procedure) f

i.e., v [t] = v [t](f(θ[t]))