The success of a machine learning (ML) project can often come down to the framework it uses. Different systems fit different situations and users, so selecting the proper library is an important step in delivering the desired results. In that spirit, here’s a comparison of two of the most popular ML frameworks — TensorFlow vs Keras.
TensorFlow is the most widely used machine learning library globally, according to GitHub data. It’s an open-source, end-to-end framework from Google, with a focus on scalability, flexibility and agile development. As such, it supports multiple languages, including Python and C++, virtually all operating systems, and offers multiple abstraction levels.
Pros of TensorFlow
The primary benefit of TensorFlow is its versatility. The framework’s complex architecture and support for so many different applications and platforms make it a good fit for many project types. As you might expect from the framework that supports Google’s projects, it’s also highly scalable and delivers impressive speeds.
TensorFlow also offers granular control and supports both high-level and low-level APIs. As a result, it can allow more flexible, agile model development if you understand how to use these controls.
Since Tensorflow has such a broad user base, there’s also plenty of documentation and support to help developers use the framework. There are 75 TensorFlow user groups around the world, offering support in 15 languages.
Cons of TensorFlow
While TensorFlow’s complexity and size make it scalable and high-performing, they also make it less accessible. Despite the large community and surrounding support documentation, TensorFlow has a steep learning curve. Considering how 42% of organizations using AI cite a lack of talent and skills as their most significant barrier to AI implementation, that can be an issue.
This inaccessibility can get in the way of TensorFlow’s speed. While the development process itself may be faster on TensorFlow than some other platforms, you can only capitalize on that if you understand the system well. Businesses and users without advanced Python knowledge or experience with the framework may struggle to experience its most promising benefits.
Keras, the second-most popular ML library, is a wrapper for the TensorFlow framework. While it builds off of TensorFlow, it isn’t necessarily a part of it, as it can also work as a front end for frameworks like CNTK and Theano. The API focuses on accessibility and visibility, making it easier to manage otherwise complex ML tasks.
Pros of Keras
The most significant advantage of using Keras is that it’s user-friendly. Keras’s intuitive user interface makes it much easier to develop ML models with minimal experience. By simplifying user controls, it also provides more visibility into models and their development, enabling faster, less involved troubleshooting.
This accessibility is increasingly important as companies from more industries pursue ML projects. For example, insurers with little programming experience can use Keras to understand and build models to quantify and categorize the 20% of people in the U.S. who qualify for services such as Medicaid, or small businesses starting their own websites could use the framework for building recommendation systems similar to those of Netflix and Instacart — who also happen to use Keras.
Alternatively, hobbyists or those new to the industry could use the platform as a stepping stone to gain more experience and skills in ML development.
Keras is also modular by design. Users can easily create custom building blocks using high-level APIs, streamlining the development process. Error messages help less experienced developers catch mistakes earlier, too.
Cons of Keras
Keras’s user-friendliness comes at the cost of TensorFlow’s performance and control. Since the framework is an abstraction layer over TensorFlow’s high-level APIs, users lose access to low-level APIs. While it may be easier to manage models on this platform, you won’t be able to do as much on it.
Given Keras’s simplicity, it also isn’t suitable for larger datasets. Models built on Keras may quickly become slow when working with more complex or sizable datasets, limiting their utility and scalability.
TensorFlow vs Keras: Which Is the Best ML Framework?
Whether Keras or TensorFlow is better for you comes down to your specific use case. Generally speaking, you should use TensorFlow if scalability and power are most important and Keras if rapid development and accessibility are more pressing.
If you have more experience in building ML models, it may be faster to use TensorFlow, since it has more resources and fewer layers of abstraction. However, Keras is faster for less experienced teams. Consequently, which framework is better may come down to the experience and skill level of the users.
The ongoing data analytics skill shortage means that Keras may be the best option for most small companies or businesses that are new to data science. Organizations that need more powerful, versatile ML models and have the necessary skills should prefer TensorFlow.
Find the Best ML Framework for Your Applications
The first step to choosing the right ML framework is understanding what different options offer. When you know the pros and cons of Keras vs TensorFlow, you can look at your needs to determine which best suits your situation. You can then develop the best ML model possible.
At ODSC Europe 2022 coming up this June 15th and 16th, there will be plenty of sessions related to TensorFlow, Keras, and other machine learning frameworks.
- Not Just Deep Fakes: Applications of Visual Generative Models in Pharma Manufacturing: Guglielmo Iozzia | Associate Director – Business Tech Analysis, ML/AI|MSD
- A Hands-on Guide to Machine Learning with TensorFlow: Laurence Moroney|Lead Artificial Intelligence Advocate|Google
- How to Teach Our World Knowledge to a Neural Network?: Oliver Zeigermann|Consultant|Self-employed
- Towards a Scalable Deployment of AI Models via Uncertainty Quantification: Christian Leibig, PhD|Director of Machine Learning|Vara
- Next Generation Web Apps: Create a Machine Learning Powered Smart Cam in the Browser with TensorFlow.js: Jason Mayes|Senior Developer Advocate for Tensorflow.js|Google
- Machine Learning for Economics and Finance in TensorFlow 2: Isaiah Hull, PhD | Senior Economist | Research Division of Sveriges Riksbank
- Revealing the Inner Self: Automatic Differentiation (Autodiff) Clearly Explained: Carl Osipov | Director, AI / ML Practice | Cognizant
- Sentiment Analysis Tricks with Keras, spaCy and Transformers: Duygu Altinok, PhD | Senior NLP Engineer – author of bestseller Mastering spaCy | Deepgram
- Introduction to Linear Algebra for Data Science and Machine Learning With Python: Hadrien Jean, PhD | Data and Machine Learning Scientist