Comparing fastai and Keras

Table of Contents

1. Introduction
2. High-Level Similarities and Differences
   1. Framework Overview
   2. Optimization and Customization
3. Fastai: Pros and Cons
   1. Pros
   2. Cons
4. Keras: Pros and Cons
   1. Pros
   2. Cons
5. Code Comparisons: MNIST Application
   1. Data Preparation
   2. Model Training
6. Conclusion

Introduction

In this article, we will compare two high-level APIs for deep learning: Fastai and Keras. Both frameworks are widely used in the field of deep learning and have their own unique features and benefits. We will explore the similarities and differences between these two APIs, look at code comparisons for a simple MNIST application, and discuss the pros and cons of each framework. By the end of this article, You will have a better understanding of which framework may be better suited for your deep learning needs.

High-Level Similarities and Differences

Framework Overview

Fastai is built on top of PyTorch and is designed to provide fast results, even for beginners. It offers a layered API that allows for easy customization as your skills improve. Fastai is known for its emphasis on intelligent defaults and is the backbone of popular deep learning courses by Jeremy Howard. On the other HAND, Keras is an official high-level API for TensorFlow, which is widely used for deep learning. It has a large user community, is the subject of numerous books and courses, and is used in both learning and production environments.

Optimization and Customization

Fastai shines in terms of providing optimized defaults and convenient functions for data preparation. With just a few lines of code, you can achieve remarkable results, even close to state-of-the-art, making it ideal for beginners or those looking for quick results. The layered architecture of Fastai allows for easy growth from a beginner to an expert, as you Delve deeper into the underlying PyTorch framework. However, Fastai has a smaller community compared to Keras, and its documentation can be inconsistent, leading to longer resolution times for certain problems.

Keras, on the other hand, has a massive user community, making it easy to find examples and get support. It has a consistent and orderly evolution and excellent documentation. However, due to its extensive options and flexibility, it may take more time for beginners to get started compared to Fastai. The integration between Keras and TensorFlow is well-established, although it requires a steeper learning curve compared to the gradual progression of Fastai's API.

Fastai: Pros and Cons

Pros

• Fast results with just a few lines of code
• Easy customization and growth from beginner to expert
• Convenient functions for data preparation and analysis
• A wide range of curated datasets available

Cons

• Smaller community compared to Keras
• Limited examples of implementations in commercial production
• Inconsistent documentation that may require examining source code for understanding

Keras: Pros and Cons

Pros

• Huge user community and extensive support
• Well-established in commercial production
• Consistent and orderly evolution
• Excellent documentation provided by the Keras community

Cons

• More complex for beginners compared to Fastai
• Requires managing more options and knobs
• Steeper learning curve due to the integration with TensorFlow

Code Comparisons: MNIST Application

To better understand the differences between Fastai and Keras, we will compare the code for a simple MNIST application.

Data Preparation

Preparing the MNIST dataset is straightforward in both frameworks, with a minor difference being the requirement to mount the drive in Fastai when using Google Colab.

Model Training

In Keras, each layer of the model needs to be defined individually, which can result in more lines of code and increased complexity. In contrast, Fastai simplifies the process by using a single parameter, resnet, to define the model's architecture.

Conclusion

Both Fastai and Keras are powerful frameworks for deep learning, each with its own strengths and weaknesses. Fastai offers fast results, intelligent defaults, and convenient functions for customization, making it ideal for beginners or those seeking quick results. Keras, with its extensive user community and solid integration with TensorFlow, provides a more flexible and mature platform for deep learning. Ultimately, the choice between Fastai and Keras depends on your specific needs and preferences.

Highlights

• Fastai and Keras are high-level APIs for deep learning.
• Fastai is built on top of PyTorch, while Keras is built on top of TensorFlow.
• Fastai offers fast results and convenient customization, making it ideal for beginners.
• Keras has a large user community and is widely used in commercial production.
• Fastai has a smaller community and inconsistent documentation compared to Keras.
• Both frameworks have their pros and cons, and the choice depends on individual requirements.

FAQs

Q: Which framework is better for beginners? A: Fastai is generally considered better for beginners due to its fast results and simplified API.

Q: Which framework is more widely used in commercial production? A: Keras is more widely used in commercial production due to its large user community and established track record.

Q: Does Fastai provide pre-trained models? A: Yes, Fastai offers a wide range of pre-trained models that can be used for various deep learning tasks.

Q: Can I use Fastai with TensorFlow instead of PyTorch? A: Fastai is primarily built on top of PyTorch, but there are ways to integrate Fastai with TensorFlow if desired.

Q: Which framework has better documentation? A: Keras is known for its excellent documentation, while Fastai's documentation can sometimes be inconsistent.

Q: Are there any limitations with using Fastai or Keras? A: Each framework has its limitations, such as limited examples of Fastai in production or the steeper learning curve of Keras. It's important to choose the one that aligns with your specific requirements and skill level.