Keras vs TensorFlow: Comparison Between Deep Learning Frameworks


December 21, 2021

The whole world is enjoying the benefits and wonders that AI and ML have been bringing along. Deep learning, as an integral component of these technologies, is based on artificial neural networks and imitates the way human brain’s neural pathways for data processing, speech recognition, decision making, and much more.

And two well-known deep learning frameworksKeras and TensorFlow have been in the limelight, owing to their salient features and regular comparison. Let us understand them individually and then compare them both, based on important characteristics.

What Is Keras?

Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. – Wikipedia

Keras has been a competent deep learning framework and a high-level API that is created on top of a backend engine which could be either Theano, TensorFlow, or CNTK. You can easily create neural networks with the best optimization processes. Developers can get the best results from algorithms in an effective time.

Keras offers flexibility, modularity and is smooth on GPU and CPU. It is simple to debug the code, has a user-friendly and consistent interface. As a high-level neural network API, it offers faster experimentation with deep neural networks.

Organizations Using Keras:

Netflix, Google, Amazon, Uber, Microsoft, Yelp, Instacart, Square, Zocdoc, Apple, Capital One, AMD, Kroll, JPMorgan Chase, etc.

Key Features:
  • Easy to use and consistent interface
  • Composable and easy to extend
  • Fast and easy prototyping
  • Multi-platform and multi backend support
  • Python-based, easy to access and debug
  • Convolutional network support

What Is TensorFlow?

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on the training and inference of deep neural networks. – Wikipedia

Designed by Google, TensorFlow is meant to make deep learning available to users with ease. It is easy to work with and hence is popular as an open-source library. Developers often wonder what TensorFlow is used for. Majorly, it is used in applications based on mobile and embedded devices, real-world projects like robotics, numerical computations, etc.

There is a collection of workflows for training and developing models with Python or JavaScript, with TensorFlow. It offers good documentation support, community resources, multiple abstraction levels, and a symbolic math library for dataflow programming. TensorFlow has adopted Keras, but both have their individual identity and features to offer.

Organizations Using TensorFlow:

Google, AMD, Bloomberg, Snapchat, Airbnb, PayPal, Delivery Hero, 9GAG, Uber, Postclick, UpstageAI, Intel, Qualcomm, Postmates, NVIDIA, etc.

Key Features:
  • Simple model creation
  • Flexibility and control for experimentation
  • Swifter debugging with Python
  • Simple to understand with good documentation
  • Fast training and model deployment
  • Supports flexible high-order gradients


Keras vs TensorFlow: A Side-By-Side Evaluation

Parameters Keras TensorFlow
Developed By Francois Chollet Google Brain Team
Definition Keras is a high-level neural network library that executes on top of TensorFlow TensorFlow is a comprehensive open-source framework for multiple machine learning tasks
Architecture High-level architecture API and simple architecture, readable and concise Low-level architecture API and complex architecture
Written In Python C++, Python, CUDA
Ease of Use More user friendly and easy to use Not as easy to use as Keras
Functionalities General-purpose operations to create deep learning models Advanced functionalities for creating neural networks
Debugging Easier to debug Difficult to debug
Data Sets It has smaller data sets It has larger data sets
Complexity Facilitates model development with ease and makes it comfortable Users need to handle computations making it complex
Development Time Fewer lines of code, faster development time Development is time is more since more coding involved
Performance Based on backend engines and hence slow in performance Has a focus on lessening cognitive computational load hence faster performance
Debugging Capacity Keras has lesser backend computation and debugging the code is easier with API debug tool – TFDBG There is code involved and hence debugging is done through the TensorFlow debugger
Trained Models Keras has trained models TensorFlow has trained models
Best For Quick implementations, rapid prototyping, multiple backend support Deep learning and complicated tasks, high performance, object detection, functionality
Community Support Less but growing community support Large community support
Popularity Keras tops the popularity chart TensorFlow follows Keras on the popularity chart
User Friendliness Keras in novice-friendly TensorFlow is tough for novices

TensorFlow vs Keras: When To Use What?

Use Keras when:

  • There is a need for prototyping
  • You are working with fresh projects with smaller data sizes
  • There is a need to use deep learning for better features
  • You want to learn fast and easily

User TensorFlow when:

  • There is a rendering of heavy projects, object detection
  • There is a broader spectrum of functionalities
  • You work in an industry segment
  • You need a high level of performance with good scalability
As We Wind Up

AI and ML are two large areas that embed a lot of scope and diversity in their applications. While Keras is meant mainly for deep neural networks, TensorFlow is for machine learning applications.

The choice of framework depends upon what the objective of the entire project is, the size of the datasets, and the level of skilled resources available. Keras and TensorFlow can be used together also, asking for the best of both worlds. Finally, it is the developer’s call to finalize which one to opt for!

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