Hands-On Meta Learning with Python

Meta learning is an exhilarating research domain in the field of AI right now. With plenty of research papers and advancements, meta learning is clearly making a major breakthrough in AI. Before getting into meta learning, let's see how our current AI model works.

Deep learning has progressed rapidly in recent years with great 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 model and it will fail abruptly when we have very few data points. Let's say we trained a deep learning model to perform task A. Now, when we have a new task, B, that is closely related to A, we can't use the same model. We need to train the model from scratch for task B. So, for each task, we need to train the model from scratch although they might be related.

Is deep learning really the true AI? Well, it is not. How do we humans learn? We generalize our learning to multiple concepts and learn from there. But current learning algorithms master only one task. Here is where meta learning comes in. Meta learning produces a versatile AI model that can learn to perform various tasks without having to train them from scratch. We train our meta learning model on various related tasks with few data points, so for a new related task, it can make use of the learning obtained from the previous tasks and we don't have to train them from scratch. Many researchers and scientists believe that meta learning can get us closer to achieving AGI. We will learn exactly how meta learning models learn the learning process in the upcoming sections.

Meta learning can be categorized in several ways, right from finding the optimal sets of weights to learning the optimizer. We will categorize meta learning into the following three categories:

Now, we will see one of the interesting meta learning algorithms called learning to learn gradient descent by gradient descent. Isn't the name kind of daunting? Well, in fact, it is one of the simplest meta learning algorithms. We know that, in meta learning, our goal is to learn the learning process. In general, how do we train our neural networks? We train our network by computing loss and minimizing the loss through gradient descent. So, we optimize our model using gradient descent. Instead of using gradient descent can we learn this optimization process automatically?

But how can we learn this? We replace our traditional optimizer (gradient descent) with the Recurrent Neural Network (RNN). But how does this work? How can we replace gradient descent with RNN? If you examine closely, what are we really doing in gradient descent? It is basically a sequence of updates from the output layer to the input layer and we store these updates in a state. So, we can use RNN and store the updates in an RNN cell.

So, the main idea of this algorithm is to replace gradient descent with RNN. But the question is how do RNNs learn? How can we optimize the RNN? For optimizing an RNN, we use gradient descent. So, in a nutshell, we are learning to perform gradient descent through an RNN and that RNN is optimized by gradient descent and that's what is meant by the name learning to learn gradient descent by gradient descent.

We call our RNN, an optimizer and our base network, an optimizee. Let's say we have a model

parameterized by some parameter

. We need to find this optimal parameter

, so that we can minimize the loss. In general, we find this optimal parameter through gradient descent, but now we use the RNN for finding this optimal parameter. So the RNN (optimizer) finds the optimal parameter and sends it to the optimizee (base network); the optimizee uses this parameter, computes the loss, and sends the loss to the RNN. Based on the loss, the RNN optimizes itself through gradient descent and updates the model parameter

.

Confusing? Look at the following diagram: our optimizee (base network) is optimized through our optimizer (RNN). The optimizer sends the updated parameters—that is, weights—to the optimizee and the optimizee uses these weights, calculates the loss, and sends the loss to the optimizer; based on the loss, the optimizer improves itself through gradient descent:

Let's say our base network (optimizee) is parameterized by

and our RNN (optimizer) is parameterized by

. What is the loss function of the optimizer? We know that the optimizer's role (RNN) is to reduce the loss of the optimizee (base network). So the loss of our optimizer is the average loss of the optimizee and it can be represented as follows:

How do we minimize this loss? We minimize this loss through gradient descent by finding the right

. Okay, what does the RNN take as input and what output would it return? Our optimizer, that is, our RNN, takes as input the gradient of optimizee

as well as its previous state

and returns output, an update

that can minimize the loss of our optimizee. Let's denote our RNN by a function

:

In the previous equation, the following applies:

So, we update our model parameter values using

.

As you can see in the following diagram, our optimizer

at a time t, takes in a hidden state

and a gradient of

as

as inputs, computes

and sends it to our optimizee, where it is added with

 and becomes

for an update at the next time step:

So, in this way, we learn the gradient descent optimization through gradient descent.

We know that, in few-shot learning, we learn from lesser data points, but how can we apply gradient descent in a few-shot learning setting? In a few-shot learning setting, gradient descent fails abruptly due to very few data points. Gradient descent optimization requires more data points to reach the convergence and minimize loss. So, we need a better optimization technique in the few-shot regime. Let's say we have a

 model parameterized by some parameter

. We initialize this parameter

with some random values and try to find the optimal value using gradient descent. Let's recall the update equation of our gradient descent:

In the previous equation, the following applies:

Doesn't the update equation of gradient descent look familiar? Yes, you guessed it right: it resembles the cell state update equation of LSTM and it can be written as follows:

We can totally relate our LSTM cell update equation with gradient descent as, let's say

= 1, then the following applies:

So, instead of using gradient descent as an optimizer in the few-shot learning regime, we can use LSTM as an optimizer. Our meta learner is the LSTM, which learns the update rule for training our model. So we use two networks: one, our base learner, which learns to perform a task, and the other, the meta learner, which tries to find the optimal parameter. But how does this work?

We know that, in LSTM, we use a forget gate for discarding information that is not required in the memory, and it can be represented as follows:

How can this forget gate be useful in our optimization setting? Let's say we are in a position where the loss is high, and the gradient is close to zero. How can we escape from this position? In this case, we can shrink the parameters of our model and forget some parts of its previous value. So, we can use our forget gate to do that and it takes a current parameter value

, current loss

, current gradient

 and the previous forget gate as the input; it can be represented as follows:

Now let's come to the input gate. We know that the input gate in LSTM is used for deciding what value to update, and it can be represented as follows:

In our few-shot learning setting, we can use this input gate to tune our learning rate to learn quickly while preventing it from divergence:

So, our meta learner learns the optimal value of

and

after several updates.

But still, how does this work?

Let's say we have a base network

parameterized by

and our LSTM meta learner

parameterized by

. Assume that we have a dataset

. We split our dataset as

and

for training and testing respectively. First, we randomly initialize our meta learner parameter

.

For some T number of iterations, we randomly sample data points from

, calculate the loss, and then we calculate the gradients of loss with respect to our model parameter

. Now we feed this gradient, loss, and meta learner parameter

to our meta learner. Our meta learner

will return a cell state

and then we update our base network

parameter

 at a time t as

.We repeat this for some N number of times, as shown in the following diagram:

So, after T iterations, we will have an optimal parameter

. But how can we check the performance of

and how can we update our meta learner parameter? We take the test set and compute the loss on our test set with parameter

. Then, we calculate the gradients of the loss with respect to our meta learner parameter

and then we update

, as shown here:

We do this for some n number of iterations and update our meta learner. The overall algorithm is shown here:

We started off by understanding what meta learning is and how one-shot, few-shot, and zero-shot learning is used in meta learning. We learned that the support set and query set are more like a train set and test set but with k data points in each of the classes. We also saw what n-way k-shot means. Later, we understood different types of meta learning techniques. Then, we explored learning to learn gradient descent by gradient descent where we saw how RNN is used as an optimizer to optimize the base network. Later, we saw optimization as a model for few-shot learning where we used LSTM as a meta learner for optimizing in the few-shot learning setting.

In the next chapter, we will learn about a metric-based meta learning algorithm called the Siamese network and we will see how to use a Siamese network for performing face and audio recognition.