Keras vs Tensorflow: Which is the Better Option?

Keras vs TensorFlow
Keras vs TensorFlow 

In recent decades, Deep Learning has become increasingly popular as a branch of Artificial Intelligence. This fascinating field has some valuable resources that will help you broaden your knowledge. There are a number of them, including Tensorflow and Keras. However, the problem is that most of you are unsure where to begin. Therefore, this article will provide a detailed comparison between Keras and TensorFlow. So, if you are interested in Deep Learning, this article is for you.

Table of Contents

What is Keras?

Keras is a powerful, open-source, easy-to-use Python library that facilitates building and testing deep learning models. Aside from its ease of learning and construction, Keras offers advantages such as broad adoption, support for a wide range of deployment options in production, and robust multi-GPU and distributed training support. It is a Python-based high-level neural network API that runs on top of TensorFlow, CNTK, or Theano.

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What is TensorFlow?

TensorFlow is a deep learning framework developed by Google with an end-to-end approach. It is a symbolic math library specializing in dataflow programming and best suited to neural network programming. For model building and training, it offers multiple abstraction levels. It is widely known for its documentation and training support, scalability, multiple abstraction levels, and support for several platforms, including Android.

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Difference between Keras and TensorFlow

Keras is an open-source wrapper for TensorFlow, Theano, and CNTK built to simplify building neural networks from scratch. It comes with a simple and easy-to-use architecture. It is used for smaller datasets and low-performance models. Furthermore, it works with Python libraries that wrap TensorFlow or Theano, which are higher-level neural network libraries. There are only minimal requirements for debugging simple networks in the Keras framework.

Meanwhile, TensorFlow is a deep learning framework specializing in dataflow programming and is best suited to neural network programming. It features a complex architecture and is used for large datasets and high-performance models. Compared to Keras, Debugging in TensorFlow is complex. It is written mainly in C++, CUDA, and Python. Furthermore, tech companies from around the world support TensorFlow.

Pros and Cons of Keras and TensorFlow

Since now you understand Keras and TensorFlow and the difference between them, let's move on to the pros and cons.

Keras

Deep learning is an artificial intelligence technique that aims to simulate the functioning of the human brain to solve highly complex problems. As part of deep learning, we use neural networks, which break down a problem into smaller pieces and solve them individually with the help of multiple operators placed in nodes. However, neural networks can be challenging to implement. As a deep learning framework, Keras handles this problem.

Here are some pros of Keras:

  • It comes with an easy-to-use consistent interface.
  • It offers easy and fast prototyping.
  • It lets you design state-of-the-art models and develop new layers.
  • It delivers actionable feedback upon user error.
  • It comes with an easy-to-use consistent interface.
  • It helps you express new ideas for research by letting you write custom building blocks.

Now, let's talk about the cons of Keras:

  • There are limited flexibility and complexity options available for this framework.
  • There is no Keras implementation of RBM (Restricted Boltzmann Machines).
  • Issues with Multi-GPU.
  • Compared to TensorFlow, it has fewer online projects.

TensorFlow

There is no doubt that machine learning is a complex discipline. However, it is much easier to implement machine learning models nowadays than ever before, thanks to machine learning frameworks like TensorFlow from Google, which simplify the process of acquiring data, training models, making predictions, and refining future results.

Here are some pros of TensorFlow:

  • It has the best community support.
  • TensorFlow helps in debugging graph subparts.
  • Compared to other platforms, TensorFlow offers better performance.
  • TensorFlow supports and utilizes many backends, such as GUIs and ASICs.
  • TensorFlow is easy to extend because you can create new ideas by adding custom blocks.

Now, let's talk about the cons of TensorFlow:

  • TensorFlow does not offer support for OpenCL.
  • Calculus fundamentals are necessary to understand TensorFlow.
  • TensorFlow is slower than other platforms of the same type.
  • There is no specific TensorFlow version designed for Windows. Instead, it is designed for other operating systems, such as Linux. However, you can install TensorFlow on Windows using a Python package installer.

Which one should you choose? Keras or Tensorflow?

Now that you know everything there is to know about Keras and TensorFlow, which one should you choose? Well, this depends on certain criteria. For beginners unfamiliar with Python, students looking for practice work, or deep learning users wanting more features, Keras is the best option.

Meanwhile, TensorFlow can be an excellent choice for those who have already worked in the industry and are familiar with Deep Learning. To conclude, you can use TensorFlow for machine learning applications and Keras for deep neural networks.


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