Frameworks play a critical role in the domain of data science. Frameworks are the collection of packages that help in streamlining the overall programming experience for building an exact kind of application. TensorFlow and Keras are among the most popular frameworks in data science.
What is TensorFlow?
TensorFlow is the most well-known library for deep learning assignments. It has very large and awesome Frameworks. The number of forks, as well as the number of commits on the TensorFlow repository, are enough to define the wide-spreading popularity of TensorFlow. However, TensorFlow is not that much easy to use for data science.
TensorFlow current version 2.5.0 runs on almost all major platforms, from mobiles to desktops, to specialized workstations, to embedded devices, to distributed clusters of servers on the cloud.
The large community experiments and pervasiveness have pushed TensorFlow into the initiative for solving real-world applications such as analyzing images, natural language processing, generating data, robotics, intelligent chatbots, and more.
Stimulatingly, TensorFlow is very useful in instrumental language translation. It is actually changing the way developers are relating to machine knowledge technology.
Google’s Rank Brain, backed by TensorFlow, holds a considerable number of inquiries every minute and has successfully replaced the traditional static algorithm that is based on search.
If you use the “Allo application”, You can reply to the last message from a few modified options. All thanks to Machine Learning with the use of TensorFlow. Another feature analyses the picture sent to you and suggests a relevant reply.
What is Keras?
Keras is the high-level library that is created on top of TensorFlow. It provides a sci-kit-learn type of API which is written in Python for building Neural Networks.
Developers can use “Keras” to quickly develop a neural network without worrying about the mathematical features of tensor algebra, optimization methods, and numerical techniques.
The key idea behind schedule to the development of Keras is to simplify the investigations by fast prototyping. The capability to go from an idea to a result with the slightest delay is key to good research.
Every organization has always tried to incorporate Deep Learning in one way or another, Keras offers a very easy and simple to use as well as in-built enough to understand API which is essential helps you to test and body Deep Learning applications with the smallest considerable efforts.
Keras vs TensorFlow – Key Differences
- Keras is very easy and simple to understand and implement. It allows you to use TensorFlow in the backend.
- However, practically one size does not fit all. Keras won’t work when you need to make low-level changes for your model. For that, you always need TensorFlow. Although difficult to understand, once you get a hold of the grammar, you will be constructing your models in no time.
- TensorFlow is an open basis software library to design a range of tasks. It is the symbolic mathematics library for Machine Learning Applications like neural networks.
- The performance of Keras is slower as compared to TensorFlow. TensorFlow provides a parallel pace which is fast and suitable for high performance.
- Keras having a simple architecture. This is more concise and readable. TensorFlow, on the other hand, is not very simple to use even still provides Keras as a framework that makes a work cooler.
- In Keras, there is generally a very less frequent need to restore simple networks. But in the case of TensorFlow, it is moderately difficult to perform debugging action.
- Normally Keras is used for small datasets as it is moderately slower and TensorFlow is used for high-performance models and large datasets that require fast performance.
- Keras is a high level of API developed on TensorFlow. Keras is most user-friendly and very simple to use as compared to TensorFlow.
Now coming to the final summary of Keras vs TensorFlow. let’s start with the situations that are most used for each one of these three deep learning frameworks
- Easy Model Building
- Advanced Functionalities
- Robust ML Production Anywhere
- Powerful Experimentation for Research
- Modular and Composable
- Rapid Prototyping
- Easy to Extend
- Easy to Use
You should extremely consider moving to TensorFlow. TensorFlow 2.5.0 and Keras in your future projects. Another readymade model is that TensorFlow 2.5.0 is more than a GPU-accelerated deep learning library.