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 used in production for deep learning assignments. It has a very large and awesome Frameworks. The number of forks, as well as the number of commits on 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 is currently available in v2.0 and its runs on almost all major platforms which are used today world, 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 analysing images, natural language processing, generating data, robotics, intelligent chatbots, and more.
Stimulatingly, TensorFlow is being used by a wide array of coders to instrumental language translation. It is actually changing the way developers are relating to machine knowledge technology.
Major Applications of TensorFlow:
- TensorFlow is effectively implemented in Deep learning – the automatic image captioning software – here developers use TensorFlow.
- Google’s Rank Brain, backed by TensorFlow, holders a considerable number of inquiries every minute and has successfully replaced the traditional static algorithm that is based on search.
- If you have used the “Allo application”, you must have seen a feature similar to Google’s Inbox. E.g. 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 in order to suggest 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.
This offer is having a huge advantage for beginners because they can dive right into Deep Learning without involving with low-level divisions.
Every organization has always tried to incorporate with 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 smallest considerable efforts.
Keras vs TensorFlow – Key Differences
- Keras is the neural network’s library which is written in Python. That is high-level in nature. Which makes it awfully simple and instinctual to use. It works as a cover to low-level libraries like TensorFlow or high-level neural network models, this is written in Python that works as a wrapper to TensorFlow. In these techniques, the comparison does not make much sense because Keras itself uses TensorFlow for the back end.
- Keras is very easy and simple to understand and implement. Using Keras is much like selling with Lego blocks for models. It was built to help the developers perform with quick tests, POC’s, and experiments before going full scale. Keras allows you to use TensorFlow in the backend that is removing the need to learn it.
- Keras was established with the impartial of permitting people to write their own writings without consuming to learn the backend in point. After all, most of the beginners would not bother about the presentation of the scripts and the details of the algorithms/maths.
- However, practically one size does not fit all when we are talking about Machine Learning Applications. The proper difference between TensorFlow and Keras is that Keras won’t work if you need to make low-level changes for your model. For that, you always need TensorFlow. Although the difficult to understand, once you get a hold of the grammar, you will be construction your models in no time.
- So, like everything, it all boils down to your necessities at hand. If you are observing to fraud around with Deep Neural Networks or it’s just that we want to build a prototype. However, if you are the one that likes to joint deep and that gets control of the low-level functionalities that you may spend some time discovering TensorFlow.
- Keras is an open-source of neural network models written in Python. It is developed to enable a fast investigation with deep neural networks.
- TensorFlow is an open basis software that library for the dataflow software design crossways a range of tasks. It is the symbolic mathematics library that is used 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 it provides Keras as a framework that makes a work cooler.
- In Keras, there is generally 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. On the other hand, TensorFlow is used for high-performance models and the 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
The first important thing is that deep learning practitioners or beginners must start to use of Keras package. That is should start using TensorFlow. (Keras inside TensorFlow 2.0.)
You should extremely consider moving to TensorFlow. TensorFlow 2.0 and Keras in your future projects. Another readymade model is that TensorFlow 2.0 is that it is more than a GPU-accelerated deep learning library.
Not only do you have the aptitude to train your own representations using TensorFlow 2.0 and TensorFlow.
Take that library and prepare them for mobile to embedded deployment by using TensorFlow Lite. Organize the models of development using TensorFlow Extended.