Every tech enthusiast who is going to start their career in the Data Science area – will search for this on google at any point in time in their journey. But, most of the time we don’t get sharp answers and we often ended up getting confused.
In this article, I will try to help you select the better deep learning library for you. We will do the analysis on different factors like architecture, speed, user-friendliness, popularity, and much more.
But, before starting with the analysis part let’s get to know about each of them in brief.
Keras
The Neural network library is available as open-source. In this symbolic math library, we solve problems related to Deep Learning and machine learning.
The purpose of this tool is to perform quick experiments using deep learning. A high-level programming API is what Keras is. This application was developed on 27th March 2015 by Francois Chollet
Pytorch
It’s a machine learning torch library that is written in Python & C++ and available for download as an open-source program.
The research group at Facebook developed this technology in Oct 2016 to be used in applications such as Natural Language Processing, Computer vision, and much more.
This program falls somewhere between TensorFlow and Keras when comparing high level to low level.
TensorFlow
The library is easily integrated with C++, Java, and other code languages. It provides comprehensive tools for Developers, Companies to build machine learning-powered applications. In this symbolic math library, deep learning and machine learning problems are solved.
In programming, TensorFlow is known as a low-level API. It was created on November 9th, 2015 by Google.
1. Architecture
Large and complex models are the most time-consuming to train, so processing speed will be less for large and complex models.
When compared to Keras, PyTorch has a more complicated architecture, which results in less readability.
The winners of this race were TensorFlow and PyTorch, which are both practical low-level simulation frameworks and fast in speed and time.
2. Speed
Keras does not operate above the minimum speed. TensorFlow and Pytorch both work at maximum speed, which in turn results in high performance.
3. Level of APIs
Keras’ APIs provide access to Theano and CNTK, so Keras can be executed on both platforms.
Because of PyTorch’s low-level API, it only supports array expressions. Recently, it has gained immense attention and has become a preferred solution for academic research and deep learning applications requiring custom expression optimization.
In addition to providing low-level APIs, TensorFlow also provides high-level APIs.
4. Beginner-Friendly
A quick prototyping feature has been designed into Keras to make the Deep Learning model easy to test. The program has an extremely beginner-friendly interface where users can easily build neural networks the way they do with Lego blocks.
Debugging Python errors is as simple as debugging Python code. Debugging these errors can be accomplished using any of the popular Python debuggers.
It is possible to debug errors in Tensorflow using its debugging module.
5. Debugging
Working with Keras is usually straightforward, and you are not likely to run into any difficulties. However, since it has too many levels of abstraction on backend platforms, debugging can often prove difficult.
With Pytorch you can debug more easily than with Keras or TensorFlow.
The debugging process for TensorFlow can be challenging.
6. Trends Popularity
Kernel-based neural networks including convolutional and utility layers are widely used by Keras. Keras is most commonly used at companies like Nvidia, Uber, Amazon, Apple, and Netflix.
Both its use of Google internally and the software it uses to capture images automatically make it famous. In addition to Google, LinkedIn, Snap, AMD, Bloomberg, Paypal, and Qualcomm, Tensorflow is used by many other companies as well.
With its NN module, optimum module, and autograd module, Keras supports high-power GPU applications, and its automatic differentiation on deep learning networks makes it popular. Among the major companies that use Pytorch are Facebook, Wells Fargo, Salesforce, Genentech, Microsoft, and JPMorgan Chase.
7. DataSet
In the original version, Keras had a slow speed and was designed for quick prototyping. The framework is therefore not well suited to processing large datasets. The reason it works well in smaller datasets is that its execution speed is fast.
While TensorFlow and PyTorch are low-level frameworks, they are capable of dealing with large datasets well because they are fast.
A high-performance task can be performed on a high-dimensional dataset with this tool.
Conclusion
To compare these 3 frameworks, we looked at a variety of parameters. While PyTorch is user-friendly and simple, TensorFlow is viewed with dissatisfaction due to its thin API.
Keras and TensorFlow have walls built up from brick and mortar, but they leave tiny openings for communication, whereas PyTorch is tightly bound to Python and can be applied to many different platforms.
Before you go…
If you liked this article and want to stay tuned with more exciting articles on Python & Data Science – do consider becoming a medium member by clicking here https://pranjalai.medium.com/membership.
Please do consider signing up using my referral link. In this way, the portion of the membership fee goes to me, which motivates me to write more exciting stuff on Python and Data Science.
Also, feel free to subscribe to my free newsletter: Pranjal’s Newsletter.