Currently, there are three primary deep learning frameworks available on the market. However, it remains unclear which of Tensorflow, Keras, or PyTorch should be used in the task. It is difficult for a novice to choose a framework for a deep learning model. 

With the comparison of three of the most popular deep learning frameworks, Keras vs. PyTorch vs. TensorFlow, the article here will help you decide which one is most suited to your needs and goals. 

What is a Deep learning framework?

It is an interface, library, or tool that makes it easier and faster to develop deep learning models by eliminating the need to understand algorithms in depth. A set of pre-built and optimized components are used to define models concisely and easily.

An appropriate framework can help us develop such a model rapidly, rather than coding hundreds of lines of code from scratch. A practical deep learning framework should have the following fundamental characteristics:

• Improved performance
• Simple to understand and code
• Good community support
• Reduce computations by parallelizing processes
• The gradients are automatically computed

Let’s start your deep learning journey by knowing three perfect frameworks for your career needs with a basic introduction and comparison factors.

Introduction

• Keras is a Python-based open-source neural network library. On top of TensorFlow, it is good enough to run. 
• TensorFlow is a Google-developed open-source software library for Dataflow programming across various tasks. 
• As a Python-based version of Torch, PyTorch is an open-source machine learning library created by Facebook’s AI research group. 

Key comparison factors

In addition to sharing several similarities, the three frameworks also differ in several fundamental ways. In this section, we’ll talk about their competitiveness.

1. Level of API

• On top of TensorFlow, CNTK, and Theano, Keras provides an API with a high-level interface. Due to its ease of use and syntactic simplicity, the framework has acquired popularity and facilitated rapid development.
• In addition to high-level APIs, TensorFlow also provides a low-level API set.
• In contrast, PyTorch is a lower-level API that focuses on array expressions. The previous year has attracted a great deal of attention, becoming a favored solution for academic research.

2. Speed

• With an R interface, Keras supports Python.
• In addition to Python, TensorFlow now offers C++, Java, Go, and Swift.
• Currently, PyTorch is compatible with Python, C++, and Java.

3. Architecture

• In addition, Keras’s architecture is clear, compact, and easily understandable, but its performance is poor. 
• TensorFlow is inflexible to use, but Keras performs better with it. 
• Despite Keras, PyTorch has a more complicated architecture that is more difficult to understand.

4. Ease of use

• Because of its incomprehensible API, TensorFlow is regularly criticized. 
• PyTorch is a lot more user-friendly and easier to work with. This framework is more strongly connected with Python. 
• TensorFlow can make you feel like your model is hiding behind a brick wall, with only a few tiny holes to communicate through at a time. So that’s why Keras.

5. Debugging

• The necessity to debug virtual networks is rare in Keras. 
• Tensorflow, on the other hand, makes debugging a challenge. 
• However, PyTorch offers superior debugging functions than the other two.

6. Beginner-friendly

• Keras is easy to work with but complex to debug due to its several abstraction levels.
• When it comes to using TensorFlow, it’s even more complicated than Keras. Tensorflow offers a debugging module that you can use to debug the problems.
• PyTorch is as easy to use as debugging Python errors. It can use any standard Python debugger to fix the errors.

7. Dataset

• Keras does well with a smaller dataset because its execution time is slow compared to other algorithms.
• TensorFlow is highly capable of managing large datasets as it has a maximum speed of execution.
• In a higher-dimensional dataset, PyTorch can handle a high-performance task.

8. Supported languages

• Keras is incredibly simple to learn and program. It was designed to make deep learning experimentation and prototyping simple. 
• TensorFlow and PyTorch, on the other hand, are not beginner-friendly because they are more challenging to grasp and develop than Keras. 

9. Performance in Neural networks: 

• Fully connected layers GRU, and LSTM, are utilized in Keras to build recurrent neural networks.
• In PyTorch, recurrent networks can be built at the multilayer and individual level classes, respectively. There is no need to optimize the cell further because of the multiple layers of the network. Data input and timestep are managed by the object of the layers, which also represents RNN with bidirectional features. 
• For deep neural networks, TensorFlow has dropout wrappers, multiple RNN cells, and cell-level classes. 

10. Coding language

• In addition, Keras is available as a coding language. In a single line, all the codes are written.
• TensorFlow is compatible with C, C++, Java, and other coding languages. Programming it with shortcodes improves its accuracy.
• Scripts for PyTorch are written exclusively in Python. PyTorch’s code is written in bigger lines.

11. Applications

• In neural networks, Keras is designed to execute robust experiments.
• TensorFlow is used to educate the machine on a variety of computational approaches.
• PyTorch is used to develop neural networks and natural language processing systems.

12. Popularity

There has been an expansion of Deep Learning technology in the business with the growing need in Data Science. All three frameworks have gained much popularity as a result of this. Keras is at the top of the list, followed by TensorFlow and PyTorch. Its simplicity, compared to the other two, has made it extremely popular. 

Conclusion

Python Torch is easy to use, but TensorFlow is criticized for its difficult-to-understand API. Keras and TensorFlow have a robust brick wall but leave small communication loopholes. In contrast, PyTorch is securely bound to Python and appropriate for many applications. Understanding of deep learning’s key concepts is transferable. Once you’ve mastered the principles in one environment, you’ll be able to apply them in other environments and hit your goals when switching to different deep learning libraries.