Keras vs TensorFlow – What to learn and Why? : All you need to know

• In this article you will learn:
• 1. What is Keras?
• 2. Keras’ characteristics.
• 3. The Advantages that keras has.
• 4. Architecture of Keras.
• 5. TensorFlow.
• 6. The Architecture of TensorFlow and Its Applications.
• 7. Keras Vs TensorFlow.
• 8.Conclusion.

What is Keras?

Python is the programming language used to create the Keras Application Programming Interface (API), which is a powerful high-level neural network API. This open-source neural network library was developed with the goal of facilitating rapid experimentation with deep neural networks. It is compatible with CNTK, TensorFlow, and Theano and can operate on top of any of them.The modularity, user-friendliness, and extendability of Keras are its primary emphasis. It delegates responsibility for low-level calculations to a separate library known as the Backend rather than doing such computations itself.In the middle of 2017, Keras was officially accepted and included into TensorFlow. It may be accessed by users using the tf.keras module. Despite this, the Keras library is still capable of functioning independently and autonomously.

Keras Characteristics:

• Simple, expandable, and consistent API.
• It is compatible with a variety of backends and platforms.
• Because of its Configurable foundation, it is capable of running on both the GPU and the CPU.
• The calculations are quite amenable to scaling.

The Advantages of Using Keras:

• It is simple to put to the test.
• Keras uses the programming language Python, which is simple and straightforward, for the development of neural networks.
• It supports convolutional as well as recurrent network architectures.
• It has widespread support from the community.
• Because the models of deep learning are independent building blocks, it is possible to combine them.

Architecture of Keras:

There are two different kinds of Keras Models, and they are as follows:

Sequential Model: The Keras Layers are arranged in a progressive fashion here. All of the currently known neural networks may be represented using the sequential model.API that is functional.It is useful in the creation of intricate models.

Layers: Each layer of Keras that is included in the Keras model is a representation of the same layer that is included in the actual neural network model. The following is a list of the important Keras Layers:

• Pooling layers.
• Layers of Convolutional Data.
• The Innermost Parts.
• Layers That Keep Coming Back.
• Core Modules.

Keras offers a number of functions for neural networks; these functions are as follows:

Activations Module: This component offers a wide variety of activation capabilities, including relu, softmax, and others.

Optimizer Module: This component offers many optimizer functions, such as sgd, adm, and so on.

Regularizers: L 1 Regularizer and L 2 Regularizer functions are supplied by Regularizers.The loss module provides many loss functions such as Poisson and mean absolute error, amongst others.

TensorFlow:

• TensorFlow is a library that is freely available and may be used to construct and train machine learning models. To be more explicit, it is a library for symbolic mathematics.
• TensorFlow is an all-inclusive platform that is suitable for both novices and seasoned programmers alike. TensorFlow is not just adaptable, but it also provides many levels of abstraction for its users.
• Its application programming interfaces work at both high and low levels. Because the Google Brain team is responsible for developing TensorFlow, there is help in the form of both documentation and training.
• The creation of data flow graphs may be accomplished using TensorFlow. A structure like this one involves the movement of data through a graph that performs processing at each node.
• Mathematical operations are represented by these nodes, and the connections between them are a tensor or a multidimensional data array. In these procedures, learning takes place, which may then be used to train neural networks for machine learning, natural language processing, or even simulations based on partial differential equations.
• Image recognition, word embeddings, recurrent neural networks, handwritten digit classification, and sequence-to-sequence modelling are some of the applications that are possible with these neural networks.
• Python itself is not responsible for performing the necessary mathematical computations. They are instead written in C++, which is a sophisticated and high-performance programming language, where they are compiled into binaries. Python is only an abstract programming language that is used to connect things together.
• The fact that it is written in Python makes it simpler for software engineers to pick up. Having saying that, learning TensorFlow for the first time might still be challenging given to the extensive intricacy of the included material. Given they have surmounted the first learning curve, anybody who is committed to learning may do so once they have access to the many courses and material.

The Architecture of TensorFlow and Its Applications:

In order to have a firm grip on the idea of architecture, we will go through the most important parts of TensorFlow:

Loaders: In the process of introducing new algorithms and data backends, the Loaders serve as the continuation point. One example of this kind of algorithm backend is TensorFlow, which may be used for the deployment of machine learning models using Tensorflow.

Servables: TensorFlow Clients will apply the Servables that they have been given in order to carry out the calculation. A single servable may add anything from a lookup table to a single model to a tuple of deducing models. It’s up to the user to decide what they want to add.

Versions that can be served and streams: We are able to manage several versions of servables since we are use TensorFlow serving. During the course of service, customers may make inquiries about either the most recent version or a version id for a particular model. Tensor APIs, which are employed for distributed runtime, are being used in the management of the Stream.

Models: TensorFlow serving may be used to define a model as either a single servable or a model that contains several servables. A part of a model may be represented by a servable if necessary.

Batcher: Batcher helps reduce the cost of implementing inference by a large amount, particularly when hardware accelerators like GPU are present. Classification, perception, comprehension, understanding, uncovering, forecasting, and creativity are among the significant applications that may be made of the library. These components provide assistance to developers in carrying out a variety of activities, including voice recognition, text recognition, video identification, and many more.

Keras Vs TensorFlow:

Keras	TensorFlow
A high-level application programming interface (API), Keras is currently operating on top of TensorFlow, CNTK, and Theano.	TensorFlow is a framework that provides access to both high-level and low-level application programming interfaces.
If you are familiar with the Python programming language, using Keras will be a breeze for you.	You will need to educate yourself on the syntax involved in utilising the different Tensorflow functions.
Perfect for fast implementations.	Perfect for use in research on deep learning and complicated networks.
Employs the use of an additional API debug tool such as TFDBG.	When debugging, you may make advantage of the visualisation features provided by a tensor board.
It was Francois Chollet who initiated it as a project, and a number of individuals were responsible for its development.	The Google Brain team was responsible for its creation.
Wrapper for Theano, TensorFlow, and CNTK that is written in Python.	Composed mostly of code in C++, CUDA, and Python.
Keras is distinguished by its straightforward, legible, and condensed architecture.	Tensorflow is not particularly user-friendly in its operation.
It is not necessary to debug basic networks in the Keras framework nearly as often as it is in other frameworks.	Debugging with TensorFlow may be a very difficult process to go through.
In most cases, a tiny dataset is all that is required for Keras.	TensorFlow is used for applications requiring high-performance models and extensive datasets.
There is not much support from the community.	It has the support of a significant number of different technological businesses.
It is possible to utilise it for models with poor performance.	It is used for the construction of high-performance models.

Conclusion:

Due to the fact that Keras was designed to be built on frameworks like as TensorFlow, comparing it to TensorFlow is not the simplest thing to do. Keras is more simpler to use than TensorFlow, despite TensorFlow’s ability to do a greater variety of tasks. TensorFlow is far more difficult to comprehend and debug than Keras, which has straightforward networks that are straightforward to troubleshoot. Keras is a lot simpler language to pick up for first-timers. However, TensorFlow offers many additional features, making it suitable for more sophisticated applications.