Are you confused between Keras and TensorFlow and can’t decide which library will suit your project requirements? Don’t worry!
This article will explain the difference between Keras and TensorFlow along with their similarities, advantages, and disadvantages, empowering you to make the appropriate decision for your artificial intelligence projects.
Keras and TensorFlow are both sought-after software libraries used for designing, training, and deployment of machine learning neural networks.
You might be conscious of any of these technologies as a programmer, developer, or engineer. However, understanding the key contrasts between these two will help you make the right choice.
In simple words, TensorFlow is an open-source and full-fledged machine-learning library developed by Google, while Keras is built on TensorFlow.
So, let’s dive deeply into the difference between Keras and TensorFlow and decide when you need to use Keras vs TensorFlow, depending on your requirements.
Purpose Of This Article
This article will give you a basic understanding of Keras and TensorFlow, highlighting the relationship between them. It looks after every aspect that you might consider when choosing a software library for your machine-learning projects, helping you make informed decisions.
We will discuss situations where you need to use Keras vs TensorFlow and where only TensorFlow will be suitable for the job.
Without any further delay, let’s get into the Keras vs TensorFlow by understanding the basics of both technologies.
Understanding The Basics
Keras and TensorFlow are both excellent tools used to build intelligent systems. They facilitate the process of designing and deployment of AI models.
What is Keras?
Keras is an open-source neural network library developed by Francois Chollet, a Google engineer. It is written in Python with an easy-to-use interface for quick deployment. It is created in such a way that developers can design, train, and deploy deep learning models effortlessly.
Keras can run on popular frameworks like PyTorch, TensorFlow, CNTK, JAX, MXNet, and Theano. So, when you use Keras, you use TensorFlow (or any other library in the backend) with an intuitive interface.
The primary purpose of Keras is to make machine learning simpler for humans with better speed, maintainability, deployability, and uncomplicated coding.
Keras allows developers to deploy models across various platforms, such as mobile devices, browsers, servers, embedded systems, etc.
Advantages and Disadvantages of Keras
Let’s now have a look at the advantages and disadvantages of Keras.
Advantages of Keras
- Keras is essentially known for simplicity and ease of use, making it easier for both beginners and experienced developers. With Keras’s user-friendly API, beginners can easily streamline deep learning tasks.
- Keras supports multiple backends like TensorFlow, PyTorch, Theano, and Microsoft CNTK, so you can choose your favored backend for your task.
- With the Keras high-level API, rapid experimentation and model deployment are possible.
- Keras boasts pre-trained models that you can modify for your tasks saving time and effort.
- The flexible architecture of Keras promotes customization.
- Another advantage of Keras is its active community that provides assistance, tutorials, and learning resources.
Disadvantages of Keras
- Low-level control is limited in Keras compared to TensorFlow.
- Keras can work slower when performing complex tasks or training large-scale models.
- The selected backend affects the performance of Keras.
- Debugging Keras projects might be difficult as its error messages are not sufficient to understand the primary cause of a problem.
What is TensorFlow?
TensorFlow is a full-fledged open-source software library designed by the Google Brain team. It was released in 2016 to ease the process of building machine-learning applications. Unlike Keras, TensorFlow includes both high-level and low-level APIs making it more efficient for machine learning projects.
TensorFlow uses data flow graphs for numerical computations and is a bit more complex than Keras. Like Keras, TensorFlow is written in Python and supports other languages like CUDA and C++.
Advantages and Disadvantages of TensorFlow
Let’s now discuss the key advantages and drawbacks of TensorFlow.
Advantages of TensorFlow
- TensorFlow is a scalable platform, so it can handle complex projects and large-scale datasets effectively. It can be used across desktop, mobile, web, and cloud applications due to its versatility.
- It has a powerful visualization tool called TensorBoard that helps in debugging and analyzing models.
- TensorFlow’s flexibility is top-notch, providing access to both high-level and low-level APIs.
- Its active community provides great support and resources for users.
- TensorFlow supports various programming languages, allowing users to select their preferred language.
- For certain tasks, it can perform faster than CPUs and GPUs due to its TPU acceleration feature.
- TensorFlow is an open-source platform, so it is free to use anywhere, anytime.
- It uses both CPU and GPU systems so that developers can choose the appropriate architecture depending on their needs.
Disadvantages of TensorFlow
Despite the above advantages, TensorFlow has some drawbacks as well, and those are as follows.
- Compared to Keras, TensorFlow’s learning curve can be steeper for beginners.
- Coding in TensorFlow can be lengthy compared to high-level APIs like Keras.
- It provides limited features for Windows users than for Linux users.
- TensorFlow’s TPU architecture supports only model execution, not training.
- It supports only Python for GPU programming and NVIDIA GPUs. So, people who use other GPUs may not like this platform.
Relationship between Keras and TensorFlow
While the purpose of Keras and TensorFlow is different, they complement each other in their true essential nature. For instance, Keras, the high-level API, is built on top of TensorFlow and can use other software libraries as backends.
Keras offers a user-friendly interface for rapid experimentation and deployment while TensorFlow functions in the background. Moreover, Keras is the default API for TensorFlow and is recommended by the TensorFlow team to use.
The key points to remember are Keras acts as the front-end tool with an intuitive interface to make the ML tasks simpler. TensorFlow acts as the backend tool to perform complex computations and other backend tasks. In this system, Keras can utilize the features of TensorFlow to provide more abilities to the user.
Therefore, we can conclude that Keras is a high-level API perfect for designing and experimenting with neural networks quickly and always requires a backend. On the other hand, TensorFlow is an end-to-end platform that can handle all functionalities single-handedly.
In the meantime, you can switch from one platform to another depending on your use cases.
Use cases of Keras vs TensorFlow
In this section, I will discuss some of the popular use cases of Keras and TensorFlow.
Use cases of Keras
Quick experimentation and prototyping
Keras acts as a high-level API for TensorFlow and other libraries, providing a seamless interface for model development. Because of this, Keras is the perfect choice for many developers for rapid experimentation and prototyping of neural networks.
Working with small to medium-sized projects
Keras makes a perfect choice for projects that feature small to medium-sized datasets or less complex models. Its user-friendly features make it easier to develop neural networks without getting into low-level APIs.
Providing a user-friendly interface for developers
Officially, Keras is the high-level API recommended by TensorFlow, and combining these two libraries offers excellent simplicity and power. So, using Keras with TensorFlow is highly beneficial for anyone who looks for simplicity with the unique features of TensorFlow.
Learning Purposes
The flexible and user-friendly design of Keras makes it a great choice for beginners to learn how to build AI models. This is why it is used for educational purposes.
Natural Language Processing (NLP)
Keras is used in natural language processing for various tasks such as text classification, sentiment analysis, language modeling, etc.
Image Recognition
Keras is also used in image recognition tasks such as image classification and object detection.
Use cases of TensorFlow
Large scale applications
TensorFlow is an end-to-end platform with excellent scalability and production abilities, so it is a perfect choice for production environments. Its ability to handle big datasets and complex models makes it ideal for enterprise applications.
Parallel processing tasks
TensorFlow’s distributed training feature is capable of training models across multiple CPUs, GPUs, and TPUs. This makes TensorFlow ideal for parallel processing tasks.
Maximized control
TensorFlow gives access to low-level APIs to fine-tune and optimize the models depending on your needs. Developers who need maximized control over their neural network models for improved efficiency should choose TensorFlow for their applications.
Computer vision
TensorFlow is used for various computer vision tasks like image generation, image segmentation, object detection, etc.
Multi-device deployment
TensorFlow’s flexible and scalable system allows the deployment of models on mobile devices and embedded systems. Developers who need this facility should consider using TensorFlow.
Certainly, Keras is used for smaller projects and fast prototyping, while TensorFlow is used for large-scale, complex, and production applications due to its scalability and powerful features.
The Architecture of Keras vs TensorFlow
Keras and TensorFlow are both open-source deep learning libraries used to develop neural networks. However, they feature dissimilar architectures, which make them ideal for different applications. Let’s now discuss the architectural difference between Keras and TensorFlow.
Architecture of Keras
Keras is a high-level API designed for simplicity and flexibility. So, its architecture is designed accordingly.
- Keras features a set of layers called modular components that function as fundamental building blocks of neural networks. These layers can be combined to build complex models.
- These layers can be stacked sequentially or functional to build models, making the architecture simple and flexible.
- Complex architectures can be structured into shared or branching layers to ensure flexibility, modularity, and reusability.
- Keras features a backend-agnostic design that runs on top of different libraries such as TensorFlow, PyTorch, JAX, Theano, or CNTK.
- The Keras architecture supports various loss functions to measure the predicted and actual output during training. This allows developers to choose the appropriate loss function for the machine learning task.
Architecture of TensorFlow
As a more flexible and powerful platform, TensorFlow has a scalable architecture to process all types of machine learning projects.
- TensorFlow uses static computation graphs where computations are represented as a series of operations. This shows how data flows through neural networks, simplifying the computation process.
- The fundamental data structures of TensorFlow are called tensors. They carry data between operations and show the input and output data in a computation graph. Tensors are also known as multidimensional arrays.
- TensorFlow manages computational graphs through sessions where a session represents the TensorFlow runtime. In a session, the tensors are evaluated, and the model parameters are optimized.
- It supports eager execution mode to execute the operations immediately without creating a graph. Also, it has Keras as the default high-level API to provide an intuitive interface for developers.
Generally, Keras highlights user-friendliness and quick prototyping, while TensorFlow focuses on advanced control and scalability. Keras features a layer-based architecture, while TensorFlow boasts data flow graphs. Therefore, Keras is more appropriate for beginners, while TensorFlow is favored for advanced developers who want to work on large-scale projects.
Performance and scalability of Keras vs TensorFlow
Performance and scalability of Keras
Keras offers excellent performance for most deep-learning tasks. However, when it comes to complex computations and large-scale applications, Keras is not as good as TensorFlow. Due to its high-level abstraction, it offers some limitations for large-scale projects.
It can handle small to medium-sized data sets and offers some limitations for large data sets.
Performance and scalability of TensorFlow
As a powerful and scalable library, TensorFlow offers high performance for large-scale applications and complex models. It optimizes CPUsand GPUs to ensure enhanced performance.
TensorFlow’s scalability is excellent in handling big data sets and distributed training across TPUs and GPUs. This platform is essentially optimized for large-scale production environments.
Ecosystem and community Keras vs TensorFlow
Ecosystem and Community of Keras
Keras boasts a simple and flexible ecosystem with some tools offering a user-friendly platform for beginner deep learning users. It focuses on providing an intuitive interface for beginners to help in fast prototyping.
It can leverage Python libraries visualization and data processing tasks. Keras features an active and supportive community with comprehensive documentation, tutorials, and forums to help newcomers.
Ecosystem and community of TensorFlow
Tensorflow has a broad ecosystem with comprehensive tools, libraries, and resources for learning, research, development, and deployment. Whether you are a newcomer, an engineer, or an advanced researcher, TensorFlow’s ecosystem has something for you.
In addition, it has a vast community of developers and researchers who contribute to the growth and support of TensorFlow. Its flexibility, scalability, and support for production environments make it suitable for various industries.
Learning curve of Keras vs TensrFlow
Keras has a gentle learning curve with its simple architecture, ensuring that beginners can understand the concepts easily. People who have experience in Python programming will find it easier to learn Keras.
You will find many tutorials and other learning resources on the official website, which will help in the learning process. People who have a background in machine learning can learn Keras in a few weeks.
It can be challenging for beginners to learn TensorFlow due to its complex architecture. Its tensor operations and computational graphs require a deeper understanding for optimal use.
However, learning TensorFlow will be rewarding due to its flexibility, control, and vast capabilities. With proper dedication to learning and consistency, one can learn TensorFlow in two to three months.
Difference between Keras and TensorFlow – head-to-head comparison
The following table will give you a quick overview of the difference between Keras and TensorFlow.
| Features | Keras | TensorFlow |
| What it is | Keras is a high-level neural network library that runs on top of TensorFlow, PyTorch, CNTK, JAX, and MXNet. It is written in Python language. | TensorFlow is an end-to-end open-source software library that performs numerical computations using data flow graphs. It is essentially used for deep learning and machine learning models. |
| Difficulty level | Easier to learn and use. | Challenging to learn and use. |
| Use Cases | Keras is used for small to medium datasets and low-performance models. It is used in reinforcement learning, natural language processing, transfer learning, etc. | TensorFlow is suitable for big datasets and large-scale projects. It is used for predictive analysis, image recognition, image synthesis, and generative models. |
| API Access | Keras is a high-level API. | TensorFlow offers high-level and low-level APIs. |
| Learning curve | Easier to learn for beginners because of its simplicity. People with a Python background will find it easier to learn Keras. | It is challenging for beginners to learn TensorFlow. However, it offers extensive documentation, tutorials, and a supportive community to help. |
| Architecture | It features a simple architecture to ensure ease of use. | TensorFlow features a complex architecture with tensors, data flow graphs, sessions, etc. |
| Community support | Its community support is limited compared to TensorFlow. | It has strong community support. |
When to use Keras vs TensorFlow?
Keras is ideal for developers who look for simplicity and a short learning curve. It is perfect for newcomers in machine learning and projects that need rapid prototyping.
It is suitable for small to medium-sized models and applications where ease of use is needed. Apart from that, Keras is also favored for various applications like natural language processing, image analysis, video analysis, etc.
Businesses working on large-scale and comprehensive machine learning projects should consider using TensorFlow. Its extensive ecosystem and computation capabilities make it ideal for handling large datasets and intricate models.
For deep learning models where advanced activities like predictive analysis, image recognition, and generative models are needed, you can consider using TensorFlow.
Organizations looking for flexibility and scalability in their projects should opt for TensorFlow.
The Bottom Line
Keras and TensorFlow are both popular deep learning/machine learning libraries. One is suitable for quick prototyping and small AI projects, while the other is preferred for scalability and extensive capabilities.
Therefore, you can consider using either of these two libraries depending on your use cases. Also, you can consider joining a deep learning or machine learning course to understand the concepts of building DL/ML models. Additionally, learning Python programming will help you understand machine learning technologies better.
Also, you can consider sharing this post with your friends and colleagues to help them decide between Keras and TensorFlow.
FAQ
Is Keras a part of TensorFlow?
Yes, now, officially, Keras is a part of TensorFlow. Keras was originally developed as a standalone library, but it was integrated with TensorFlow 2.0 in 2019. With this integration, users get the intuitiveness of keras and the power and flexibility of tensorflow in one package.
Can I use Keras without TensorFlow?
Before, it was possible to use Keras as a standalone platform. However, it is recommended to use Keras with the TensorFlow ecosystem for better performance since Keras is integrated with TensorFlow. In addition, Keras needs a backend to use.
Should I install TensorFlow or Keras first?
First, you should install TensorFlow, and let me explain why. Keras is included with the TensorFlow package, hence, you will have access to Keras automatically. With TensorFlow, you can be assured that you are using the updated and optimized version of Keras.
First, install the latest version of TensorFlow using conda or pip. Next, import the Keras module in TensorFlow to use both platforms correctly for your deep learning projects.
Is Keras faster than TensorFlow?
In short, there is no noticeable difference in performance speed between Keras and TensorFlow. Keras is built on top of TensorFlow, so when you use Keras, you use TensorFlow indirectly.
Due to the simplicity and user-friendliness of Keras, it might appear to be faster than TensorFlow. However, TensorFlow does all computations in the background.
Should I learn TensorFlow before Keras?
I would recommend learning Keras first because it is a beginner-friendly platform. Newcomers can easily understand the foundations of deep learning with Keras. It will let you deploy deep learning models quickly and can handle small to medium-sized deep learning projects.
When you want to deal with large-scale projects and do advanced customizations, you can learn TensorFlow.
Which framework is best for deep learning?
It depends on your specific use cases. If you are just starting with deep learning and don’t want to go deeper into concepts, you can opt for Keras. If you are looking for an end-to-end platform for your deep learning tasks, you can consider using TensorFlow.
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