Keras vs TensorFlow: Which should You Choose for Deep Learning?


By Faraz -

If you're looking to start learning deep learning, it can be difficult to know which framework is best. This blog article summarises the pros and cons of both Keras and TensorFlow.

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If you’re just getting into deep learning, one of the first things you’ll want to do is choose a deep learning library. There are a ton of them out there, and it can be hard to decide which one to use.

One popular choice is TensorFlow. It’s been around for a while and has a lot of supporters. However, there’s also Keras. It’s newer and less well-known, but some people think it’s better than TensorFlow. So which should you choose?

In this article, we’re going to look at the differences between Keras and TensorFlow and decide which one is best for you. We’ll also give you some tips on how to get started with either library.

What is Keras?

Keras is a library for building deep learning models. It was created by Ian Goodfellow, who is also the creator of the well-known open-source deep learning library Python for Machine Learning (PML). Keras lets you easily build neural networks and other machine learning models using a simple API.

Some of the key features of Keras include:

  1. Ease of Use: Keras is very easy to use, making it a great choice for beginners.
  2. Neural Networks: Keras supports deep neural networks, allowing you to build complex models quickly and easily.
  3. Memory Efficiency: Keras is memory efficient, meaning that you can run large models without experiencing significant performance degradation.
  4. Modularity: Keras is modular, meaning that you can split your code up into separate files for easier maintenance and modification.
  5. API: The Keras API provides a rich set of functionality that allows you to customize the library to suit your needs.

Pros and Cons of Keras

Pros of Keras:

  • Very easy to use; anyone with some basic programming skills can start using it right away.
  • High level of flexibility; allows users to easily customize their algorithms and models.
  • Robustness; has been proven to work well on a wide variety of data sets.

Cons of Keras:

  • comparatively slower than alternatives such as TensorFlow; may be unsuitable for certain applications.

What is TensorFlow?

TensorFlow is a library for building machine learning models. It was created by Google Brain, the company's research division devoted to advancing machine intelligence. TensorFlow lets you easily build complex neural networks and other machine learning models using a simple API. TensorFlow can be used for machine learning and artificial intelligence applications.

Some of the key features of TensorFlow include:

  1. TensorFlow is scalable: With TensorFlow, you can easily scale your deep learning models up to very large sizes without issue.
  2. TensorFlow is easy to use: TensorFlow is incredibly easy to use, and you can get started with it in just a few minutes.
  3. TensorFlow supports multiple frameworks: TensorFlow also supports several popular machine learning frameworks, such as Google's own PyTorch and Microsoft's AzureML. This gives you a lot of flexibility when choosing a deep learning framework to use with TensorFlow.
  4. TensorFlow provides built-in support for data preprocessing: With TensorFlow, you can easily preprocess your data before you start training your deep learning models. This makes it much easier to get started with deep learning.

Pros and Cons of TensorFlow

Pros of TensorFlow:

  • Supports large-scale deep learning tasks
  • Has a well-defined API
  • Easy to use for beginner developers
  • Can be used with multiple programming languages

Cons of TensorFlow:

  • Takes longer to train deep neural networks than Keras

Which One Should I Choose?

Keras and TensorFlow are two of the most popular libraries for deep learning. They both have their own strengths and weaknesses, so it can be tough to decide which one to use.

-First, let's take a look at some of the key differences between Keras and TensorFlow. Keras is more focused on data pre-processing and building custom neural networks, while TensorFlow is more geared towards building custom models with ready-made libraries.

If you want to build a model from scratch, Keras is probably a better option. It has a more comprehensive API and allows you to create more complex models than TensorFlow. However, if you want to use pre-built neural networks, TensorFlow is the better choice. It has a larger community of developers and more libraries available.

Overall, it depends on what you're looking for. If you're just starting out and don't have much experience building neural networks, Keras might be a better choice. If you're already familiar with neural networks and want to use ready-made libraries, then TensorFlow is likely the best option.


As deep learning continues to grow in popularity and sophistication, more and more developers are starting to use TensorFlow as their go-to tool for deep learning. However, there is another popular library called Keras that is also very powerful when it comes to deep learning. So which library should you choose? The answer depends on your specific needs and preferences. If you are primarily a data scientist who wants a high-level API that is easy to use, then TensorFlow may be the better choice for you. However, if you are more focused on building low-level code or incorporating AI into your products, then Keras may be the better fit. Ultimately, it’s important to decide which library will work best for your project before jumping into coding – otherwise you might find yourself struggling with code complexity or usability issues later down the line.

That’s a wrap!

I hope you enjoyed this article

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Faraz 😊

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