December 21, 2021
December 7th, 2022
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 frameworks – Keras 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.
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.
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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.
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|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|
Use Keras when:
User TensorFlow when:
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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