Getting Started with PyTorch and Keras

With a Python environment configured for deep learning, it's time to get acquainted with the primary tools for building an autoencoder: PyTorch and Keras. These frameworks serve as your main toolkit for constructing and training neural networks.

What is PyTorch?

PyTorch is an open-source machine learning framework developed by Facebook's AI Research lab (FAIR). At its core, PyTorch is designed for deep learning applications and is known for its flexibility and ease of use. It allows you to define, train, and deploy machine learning models, ranging from simple ones to very complex neural networks.

Imagine you have a complex mathematical operation you want to perform, especially one involving many variables that need to be adjusted to achieve a goal, like our autoencoder learning to reconstruct images. PyTorch provides the underlying infrastructure to handle these computations efficiently, especially with the help of specialized hardware like GPUs (Graphics Processing Units) if you have them. While PyTorch is very powerful and can handle intricate model designs, we'll be using it through a more approachable interface for this introductory course.

What is Keras? And How Does it Relate to PyTorch?

Keras is a high-level API (Application Programming Interface) for building and training deep learning models. The term "high-level" means it's designed to be user-friendly and intuitive, allowing you to define neural networks without getting bogged down in the lower-level details of tensor manipulations or complex mathematical implementations.

Keras was originally a standalone library that could work with several different backend engines, including PyTorch. However, with the release of Keras 3, the multi-backend support has been a major focus. This means you can use the same Keras code to run on different frameworks, including PyTorch.

Think of it this way: PyTorch is the powerful engine that can perform all the heavy lifting for your neural network. Keras provides a simplified set of controls and blueprints (like pre-made components) that make it much easier to assemble and run that engine.

This diagram illustrates how Keras acts as a user-friendly interface on top of the powerful PyTorch engine, allowing you to define your autoencoder model more easily.

For our autoencoder, Keras will allow us to:

• Define the layers of our network (input, encoder hidden layers, bottleneck, decoder hidden layers, output) in a straightforward manner.
• Compile the model by specifying the loss function (like the MSE we discussed) and the optimizer (the algorithm that helps the model learn).
• Train the model on our data with a simple command.

Why These Tools for Your First Autoencoder?

We've chosen PyTorch and Keras for several good reasons:

1. Ease of Use for Beginners: Keras, in particular, lowers the barrier to entry for deep learning. Its syntax is clear and resembles how you might describe a network's structure in plain language.
2. Industry Standard: PyTorch is one of the most widely used deep learning frameworks in both research and industry. Learning it provides you with valuable skills.
3. Rich Ecosystem: There's an amount of documentation, tutorials, and a large community, making it easier to find help and examples.
4. Integrated Experience: Since Keras can use PyTorch as its backend, they work together smoothly.

Getting Them Ready in Your Code

In the previous section, you should have installed PyTorch and Keras as part of your environment setup. To use them in your Python scripts or Jupyter notebooks, you'll first set the Keras backend and then import the necessary libraries.

The standard way to set the Keras backend is by setting an environment variable before importing Keras.

import os
os.environ["KERAS_BACKEND"] = "torch"

You will then import the Keras library.

Since Keras is a multi-backend API, you access it through the standard keras import. For example, when we start building our autoencoder, we'll import specific components from Keras like this:

from keras.layers import Input, Dense
from keras.models import Model

Here, Input and Dense are types of layers we'll use, and Model is what we'll use to define the overall structure of our autoencoder. Don't worry too much about these specific imports right now; we'll explain them in detail as we construct the model in a later section ("Constructing a Simple Autoencoder Model").

In summary, PyTorch provides the powerful backend, and Keras offers a convenient way to define and train our neural networks, including the autoencoder we're about to build. With these tools at your disposal, you're well-equipped to translate the autoencoder principles we've learned into working code.

Next, we'll look at loading and preparing a dataset, which will be the raw material our autoencoder will learn from.