Keras vs TensorFlow

The first step in developing a powerful deep-learning model is selecting the best framework. A deep learning framework is a library, interface, or tool that allows you to quickly and efficiently develop machine learning models using pre-built and reusable components. These are extremely important in the field of data science. Frameworks are a set of packages and libraries that aid the overall programming experience for developing a specific type of application.

Keras and TensorFlow are among the most well-known among the many available Deep Learning frameworks. Both Keras and TensorFlow are widely used for their ability to create neural networks for machine learning. One might be more familiar with neural networks from a data scientist's perspective, while another would be more comfortable from a programmer's.

The most common library is TensorFlow, although Keras is steadily gaining ground because of how user-friendly it is. However, you must know the differences between the two to choose one.

To help you make the right decision, we'll provide a few things about Keras and TensorFlow in this article.

Keras vs TensorFlow - Table of Contents

What is Keras? What is TensorFlow? Difference between Keras and TensorFlow Keras vs TensorFlow - Features Comparison Keras vs Tensorflow - Exploring the Advantages Keras vs TensorFlow - Disadvantages Which is Better: Keras or TensorFlow? Keras vs TensorFlow - Which one to Choose?

What is Keras?

One of the well-known open-source APIs with a neural network library created in Python is called “Keras”. It is compatible with the most popular Deep Learning toolkits, including TensorFlow, Theano, and Microsoft Cognitive. Deep neural networks can analyze data more quickly as a result.

Keras prioritizes modularity, usability, and extensibility. It does not perform low-level computations; instead, it passes them to another library known as the Backend. In mid-2017, Keras was adopted and integrated into TensorFlow. It is accessible to users through the tf.Keras module. However, the Keras library can still function independently.

Do you wish to get Certified in Keras and advance your career there? Then enroll in "Keras Online Training". This course will help you to achieve excellence in this domain.

What is TensorFlow?

A complete, open-source deep learning framework called “TensorFlow” was created by Google and released in 2015. It is renowned for its support for many abstraction layers, scalable production and deployment options, and compatibility with various platforms, including Android.

The most effective usage of TensorFlow, a symbolic math framework for neural networks, is for dataflow programming across various tasks. It provides a range of abstraction levels for model building and training.

A machine learning library designed for analytical computing is called TensorFlow. It is a platform-neutral tool. It is compatible with Central Processing Units (CPU), TPUs, and embedded platforms in addition to a Graphical Processing Unit (GPU).

TensorFlow is a promising and quickly growing entry into deep learning. It offers a flexible, complete ecosystem of community resources, libraries, and tools that make building and deploying machine-learning apps easier.

Related Article: TensorFlow Tutorial

Difference between Keras and TensorFlow

The Keras and TensorFlow frameworks have specific fundamental characteristics that set them apart and have some underlying connections that make them similar.

1. Control

• With TensorFlow, you have more control over your Keras network because of its increased versatility and advanced features. TensorFlow makes it easy to conduct various operations on weights, gradients, and other features.
• When deciding between Keras and Tensorflow, it's vital to keep in mind that, being a wrapper for the Tensorflow framework, Keras allows you to take advantage of the most significant features of both.

2. Level of API

• Keras is a high-level application programming interface that can function as a top TensorFlow. In part because of its syntactic simplicity and user-friendliness, it has acquired popularity as a tool for rapid program creation.
• High-level and low-level APIs are available in the TensorFlow framework. 

3. Origin

• The Google Brain team created the open-source TensorFlow library. Also, this library is freely available to the public. 
• And Google Francois Chollet leveraged the concepts of modularity, extensibility, minimalism, and Python to develop Keras, a simple Python toolkit for deep learning that can outperform even TensorFlow.

4. Popularity

• Rising expectations in Data Science have led to a surge in the prevalence of Deep learning in business. 
• Both frameworks have significantly benefited from this. The list's top pick is Keras, followed by TensorFlow. Its relative ease of use made it stand out from the others, quickly becoming a popular choice.

5. Architecture

• Keras is a clean architectural design. It's shorter and easier to read. 
• Contrarily, while TensorFlow does have the helpful framework Keras, it could be more user-friendly.

6. Speed

• Keras has a performance that is roughly lower than TensorFlow, which both have a comparable speed, which is fast and appropriate for high performance.

7. Dataset

• Keras is a slower option. It is reserved for use with smaller datasets. 
• TensorFlow is used for high-performance models and big datasets demanding high-speed execution.

8. Functionality

• Keras has all the standard features needed to develop deep-learning models. However, it is only suitable for some advanced users. TensorFlow is the way to go if you want cutting-edge capabilities and operations.
• TensorFlow is the best option if you need to create a unique deep-learning model.

9. Debugger

• The debugger is another area where TensorFlow excels above Keras. 
• TensorFlow comes with its custom debugger, allowing greater insight into the structure and execution status of the various TensorFlow graphs. The information gleaned from the debugger can then be used to locate and fix bugs.

Keras vs TensorFlow - Features Comparison

Key features of Keras

• Keras is a high-level API that facilitates interaction with multiple platforms and multiple backends.
• Keras helps speedy experimenting with projects that can quickly offer ready products for the market.
• It can facilitate the development of recurrent and convolutional neural networks.
• Keras has a lot of personalities. As a result, various businesses and research groups use it for their own research goals.
• It enables models to be prototyped quickly.
• Keras is employed in many fields due to its flexibility, including healthcare, business insights, sales forecasting, customer service, and virtual assistants.
• Keras is an application programming interface (API) tool that is significantly more Pythonic.
• Python was the foundation upon which Keras was built. As a result, it is simple to investigate, troubleshoot, and incorporate.
• It primarily emphasizes the quality of the user experience and the efficiency with which deep learning models are produced.
• The modularity of the Keras language is an important aspect.
• Building a deep neural network model is simpler and easier to use.

Key features of TensorFlow

• TensorFlow simplifies model development and training by providing a high level of abstraction.
• Assistance with both user-defined and higher-order gradients.
• Python tools provide for more efficient debugging.
• Quite possibly the most well-known and simple to use with Python.
• TensorFlow allows you to rapidly train and deploy your model, regardless of your programming language or platform.
• TensorFlow gives users great control and flexibility because of features such as the Keras Functional API and Model.
• Models that are dynamic using Python's control flow.
• Documented, making it simple to understand.

Do you wish to get Certified in TensorFlow and advance your career there? Then enroll in "TensorFlow Online Training".This course will help you to achieve excellence in this domain.

Keras vs TensorFlow - Exploring the Advantages

Advantages of using Keras 

Keras is a TensorFlow library that streamlines the process of deploying, building, and training neural network models. To further simplify your use of Keras, it helps to be familiar with Python. Keras' advantages are described in the following categories

1. Easy Custom Building Blocks: Keras adds a layer of abstraction on top of TensorFlow's higher-level APIs and capabilities; however, this drawback is that access to TensorFlow's lower-level APIs is no longer possible.
2. User-Friendly Interfaces: Those who haven't worked extensively with Keras or TensorFlow development will find the learning curve gentler. It helps get a model up and running quickly.
3. General Ease of Use: Keras makes the debugging process easier by providing additional feedback for the actions taken by the developer. It makes it simpler to use the entire system, going beyond what is provided via the interface.

Advantages of using TensorFlow

TensorFlow is a sophisticated library requiring a developer's in-depth knowledge of programming and machine learning techniques. TensorFlow is written in Python, just like Keras, offering Python programmers an advantage. When it comes to artificial neural networks and machine learning, Python is a common choice of language. TensorFlow's advantages can be summarized as follows:

1. Fast Model Deployment: If you understand the complexities of machine learning, you will also be able to deploy and test your models more quickly. Because Keras provides an abstraction layer, it can also serve as a work layer between the developer and TensorFlow's features.
2. Build Models Easily: TensorFlow is not difficult to understand or use. Suppose you are an experienced Python programmer and are familiar with TensorFlow. In that case, you will find that TensorFlow allows you to construct, train, and deploy a model more quickly than Keras. It is analogous to how a command-line utility can be easier to use than a graphical user interface.
3. Flexibility and Control: TensorFlow gives you access to low-level and high-level APIs, giving you more fine-grained control than you have with Keras. Keras only gives you access to high-level APIs.

To get answers to your TensorFlow Questions, Read here "TensorFlow Interview Questions"

Keras vs TensorFlow - Disadvantages

Disadvantages of Keras

• The Keras library errors did not provide the user with helpful information.
• The fact that Keras is a low-level application programming interface, is its most significant disadvantage.
• When designing some models, only a few of the pre-trained models that Keras has available have not been very supportive.

Disadvantages of TensorFlow

• TensorFlow does not support the OpenCL programming language.
• Compared to other platforms of the same type, the speed of the TensorFlow on this particular platform is significantly slower.
• Although it is simple to install TensorFlow on Windows using a Python package installer, it was created for operating systems other than Windows, such as Linux. TensorFlow was not created expressly for the Windows operating system (pip).
• The user needs to have a fundamental understanding of calculus to comprehend tensor flow better.

Which is Better: Keras or TensorFlow?

What you're trying to do with either will depend on it. Keras makes sense if you're attempting to create a straightforward neural network for quick deployment. Going straight to TensorFlow makes sense if you need to perform extensive customization or interact with low-level APIs.

Because of its simple, user-friendly interface, Keras may be a good place for beginners to start. However, not everyone who learns Keras will be exposed to every feature of the TensorFlow library.

These two models are different from one another in many important ways. TensorFlow is an open-source toolkit that may be used for various machine learning applications, whereas Keras is a library specializing in neural networks. TensorFlow offers APIs at high and low levels, whereas Keras exclusively offers APIs at the high level. Regarding adaptability, Tensorflow's eager execution enables rapid iteration in addition to straightforward debugging. Both of these features are provided by Tensorflow. Keras offers high-level APIs that are straightforward and consistent, and it adheres to industry best practices to lessen the mental strain placed on its users. Therefore, both frameworks offer high-level application programming interfaces (APIs) that make constructing and training models easy. Because it is written in Python, Keras is simpler to use than TensorFlow, a popular alternative.

Keras vs TensorFlow - Which one to Choose When?

The results show that Keras is superior to Tensorflow in -

• The flexible backend support it offers.
• Quick iteration to a market-ready prototype.
• Using simple programs and data sets suitable for novices.

There are some situations in which TensorFlow is more advantageous than Keras -

• Quickly rendering in-depth projects with little effort.
• Effortless management of projects with massive data sets.
• Object detection works better with this method.
• Provides access to a diverse range of functionalities.

Conclusion

Keras is simply an application that runs on top of TensorFlow to make the TensorFlow deployment process faster and easier. As you can see, it is challenging to compare Keras and TensorFlow since Keras is an application that operates on top of TensorFlow.

TensorFlow is more challenging to use on its own, but it does offer some benefits, such as access to low-level APIs. When it comes to constructing, training, and deploying models, you have the option of using either Keras or TensorFlow. Keras was developed to be user-friendly and straightforward, although many people will find that TensorFlow gives them greater access to more in-depth alternatives. Keras was named after its intuitive interface.

Therefore, it is dependent on the tasks that you need to complete. 

• It is always feasible to begin working with Keras and transition to TensorFlow if extra functionality must be exposed. It is always possible. 
• It is also feasible to begin working with TensorFlow and then transition to Keras if you find that TensorFlow needs to be clarified.