PyTorch vs Tensorflow vs Keras

Machine learning practitioners and software engineers typically turn to frameworks to alleviate some of the heavy lifting involved with programming.

For example, machine learning frameworks allow practitioners to implement models without needing to dig deep into the mathematical underpinnings of the algorithms.

This streamlines the development process for programmers since they aren’t required to consistently rewrite code to build their applications.

These concepts also apply to deep learning — a subset of machine learning. TensorFlow, Keras, and PyTorch are three of the most popular deep learning frameworks.

In this tutorial, we will explore each one and develop a rough solution to help you decide which one is right for you to use for your project.

What is TensorFlow?

TensorFlow is an end-to-end deep learning framework. It was developed by the Google Brain team, initially for internal use at Google for research and production; however, it was made open-source in 2015 under the Apache License. In September 2019, a new updated version of TensorFlow was released by Google under the name TensorFlow 2.0. You can explore the framework in more detail in our TensorFlow tutorial.

Some of the key features of TensorFlow include:

• TensorBoard. A visualization toolset for TensorFlow that aids in understanding, debugging, and optimizing TensorFlow programs.
• Feature columns. High-level abstractions that transform raw data into formats suitable for machine learning models, simplifying the handling of diverse data types.
• Easily trainable on CPU and GPU. TensorFlow can train models on various hardware types, including CPUs and GPUs, enabling flexible and efficient computations.
• Parallel training. TensorFlow supports distributed computing, allowing simultaneous processing of data and models across multiple CPUs or GPUs, significantly speeding up training times.

Pros of Tensorflow

Open-source

TensorFlow is an open-source framework, which means it is free of cost and can be used by anyone without having to lock into a contract with a vendor beforehand.

Debugging

One of TensorFlow’s key features is TensorBoard. TensorBoard is what provides the visualization and tooling required for experimentation in

TensorBoard provides the visualization and tooling needed for machine learning experimentation in deep learning, which makes it much easier to debug your TensorFlow code.

Keras compatibility

When Google announced the release of TensorFlow 2.0, they also declared Keras to be the official high-level API of TensorFlow. This allows users to add high-level functionality to their code and reap the benefits of Keras’ simplicity.

Scalability

TensorFlow is not limited to a single device. Users may develop systems using the framework and expect it to work as efficiently on edge devices as it works on any other complex machine.

Compatibility

The framework is compatible with several programming languages; thus, users can work in the programming language they are most comfortable with.

TensorFlow offers a reliable API for Python, along with APIs for Javascript, C++, and Java that do not guarantee backward compatibility. Additionally, there are third-party packages that provide language bindings for C#, Haskell, Julia, MATLAB, Object Pascal, R, Scala, Rust, OCaml, and Crystal.

Parallelism

TensorFlow is seen as a hardware acceleration library. The framework uses different distribution strategies in GPU and CPU systems. This means a user is free to use whatever architecture they would like to use. Note the system defaults to GPU if not mentioned explicitly; this process reduces memory usage to some extent.

Disadvantages of Tensorflow

Frequent updates

Overhead for a user to install and bind TensorFlow to their existing system is increased due to frequent updates

Inconsistent homonyms

TensorFlow offers homonyms, which are challenging to comprehend and use since they have identical names but different implementations. For example, the tf.nn.conv2d and tf.layers.Conv2d have the same name, but they have slightly different settings, which can alter your outputs.

Missing symbolic loops

TensorFlow is referred to as a low-level API — and for good reason. The framework does not automatically handle iterations that result in symbolic loops. However, it does have usage for definite sequences, thereby making it a usable system.

Limited GPU support

The framework only supports NVIDIA and Python for GPU programming. There is no additional support.

Low implementation speed

Across all hardware configurations, TensorFlow consistently requires the most time to train various kinds of neural networks compared to its competitors.

What is Keras?

Keras is a popular open-source high-level neural network API developed by François Chollet and released in 2015. The documentation refers to it as, “an API designed for human beings, not machines.”

In mid-2017, the framework was adopted and integrated into TensorFlow, making it accessible to TensorFlow users through the tf.keras module. However, it’s still possible to operate Keras independently of TensorFlow – check out this Keras cheat sheet or our full Keras tutorial.

Here’s more from the documentation:

“Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Your models run faster thanks to XLA compilation and Autograph optimizations and are easier to deploy across every surface (server, mobile, browser, embedded) thanks to TF Serving, TF Lite, and TF.js.”

Pros of Keras

Simplicity

Keras has an extremely simple, user-friendly API, which contributes to it being easy to learn and get started with deep learning.

Backend support

Low-level computations are not used by Keras to function — the framework is built on top of Microsoft CNTK, Theano, and TensorFlow. Therefore it encourages the employment of backends.

Pre-trained models

A pre-trained model is a model that’s been saved after previously being trained on a large dataset. Keras provides several pre-trained models out of the box.

Fast experimentation

Keras is a high-level API; this means it’s fairly abstracted and more generic. Therefore, users can rapidly build models in a few lines of code.

Community support

Keras is an open-source platform and has a large community of active researchers and developers who contribute to support one another.

Clear documentation

The Keras documentation is filled with coded examples, making it simple for users to quickly grasp framework concepts.

Cons of Keras

Some improvable deatures

Several practitioners have agreed Keras lacks a bit in the feature department, and there’s plenty of space for improvement. One such example is that the framework does not support dynamic chart creation.

Inefficient errors

The Keras library's error messages are ineffective. There is a requirement that the faults be clearly visible. Finding the error's primary cause is not all that useful or helpful. Keras requires a lot of debugging.

High-level API

Keras is a high-level API. Therefore, you have much less control over manipulating functions.

What is PyTorch?

PyTorch is the newest deep learning framework of the bunch. Developed by Facebook’s (now Meta) AI research group and open-sourced in 2016, PyTorch is now part of the Linux Foundation umbrella.

Over the years, PyTorch has built a reputation as a framework of simplicity, flexibility, and efficiency. It enables developers to rapidly build complex neural networks for applications such as computer vision and natural language processing.

Some of its key features include:

• Tensor computation. PyTorch provides a multi-dimensional array called Tensor, similar to NumPy's ndarray, but it can run on GPUs for faster computation.
• TorchScript. A way to serialize PyTorch models, allowing them to run in other environments like C++, ensuring production readiness.
• Dynamic graph computation. PyTorch uses dynamic computational graphs, enabling flexibility in building and modifying models on the fly, which is beneficial for complex architectures.
• Automatic differentiation. Automatic differentiation and gradient computation help simplify backpropagation and training of neural networks.
• Python support. PyTorch integrates seamlessly with the Python ecosystem, making it easier to use with Python libraries and providing a more Pythonic and user-friendly interface.

Pros of PyTorch

Pythonic

PyTorch is Pythonic in nature, which makes it quite straightforward for Python developers to adopt. This feature is a pro because a large portion of deep learning work is done in Python.

Easily to learn

The PyTorch syntax is extremely simple and intuitive. Practitioners typically credit PyTorch as having a simple learning curve — some say it’s one of the easiest deep learning packages to learn.

Easy to debug

PyTorch is capable of utilizing Python's pdb and ipdb debugging tools. Programmers may also use PyCharm, the Python IDE, for debugging as PyTorch creates a computational graph in real-time.

Strong community

Despite being one of the newer deep learning frameworks, PyTorch has been able to develop a dedicated community of developers quite rapidly. These people work extremely hard to ensure the documentation is well-structured and insightful for other developers.

Data parallelism

PyTorch’s data parallelism is extremely effective as it enables users to split data into batches and send them to various GPUs for processing. By employing this method, PyTorch is able to transfer a sizable portion of the workload from the CPU to the GPU.

Cons of PyTorch

No data visualization

TensorBoard by TensorFlow enables practitioners to visualize the training process, but there is nothing like this in PyTorch — users are required to use a third-party tool.

API server needed for production

There is no framework to provide models directly on the web; thus, you are required to use a third-party backend server like Flask or Django.

Which to Choose? A Comparative Analysis of PyTorch vs Keras vs TensorFlow

Choosing the Right Framework

The answer to “which framework is better?” is always going to be subjective since each framework has pros and cons, as we’ve seen. However, here’s a list of things to consider when deciding on a framework.

Pytorch vs TensorFlow

• Boilerplate code. Both PyTorch and TensorFlow simplify model construction by eliminating much of the boilerplate code.
• Pythonic and OOP. PyTorch is more "Pythonic" and adheres to object-oriented programming principles, making it intuitive for Python developers.
• Popularity. TensorFlow, being older and backed by Google, has a larger user base and community support.
• Flexibility. PyTorch offers more flexibility due to its use of dynamic computation graphs, allowing on-the-fly changes.
• Debugging and Tracking. TensorFlow's TensorBoard provides powerful visualization tools for debugging and tracking the training process.
• Serving framework. TensorFlow has a more mature serving system for deploying models, making it more seamless than PyTorch's deployment process.

PyTorch vs Keras

• Learning curve. Both PyTorch and Keras are user-friendly, making them easy to learn and use.
• Research vs development. PyTorch is often preferred by researchers due to its flexibility and control, while Keras is favored by developers for its simplicity and plug-and-play qualities.
• Speed and debugging. PyTorch is generally faster and provides superior debugging capabilities compared to Keras.
• Tutorials and small datasets. Keras shines with its extensive, reusable code tutorials and is particularly effective when working with small datasets.

TensorFlow vs Keras

• High-Level APIs. Both TensorFlow and Keras provide high-level APIs for building and training models.
• Pythonic nature. Keras, being built in Python, is more user-friendly and intuitive.
• Large datasets. TensorFlow is often preferred for handling large datasets due to its robustness and scalability.
• Wrapper. Keras acts as a wrapper to TensorFlow functions, simplifying the TensorFlow interface for ease of use.

Conclusion

TensorFlow, PyTorch, and Keras are three of the most popular deep learning frameworks. Each has its own unique qualities, which makes them great tools for certain areas. The one you proceed to learn and use depends specifically on your use case and other factors, such as your technical ability and how long you have to learn.

Regardless of the framework you opt for, there are a number of useful courses on DataCamp to help you get started:

• Introduction to TensorFlow in Python
• Introduction to Deep Learning with Keras
• Deep Learning with PyTorch