Keras vs PyTorch: Which ML Framework Should You Learn?

Keras and PyTorch are both open-source frameworks for designing and developing neural networks and deep learning technology. 

Specifically, Keras is a neural network platform that runs on top of the open-source library TensorFlow (or others), while PyTorch is a lower-level API designed for direct control over expressions. 

Which one is better? It depends on what you’re using the technology for. 

If you’re a scientifically and mathematically inclined individual digging deep into the framework of deep learning, PyTorch may be the better solution. If you’re a developer interested in rapid development and prototyping of deep learning technologies, Keras has more to offer.

Let’s take a deeper look into whether Keras or PyTorch is the right solution for you.

An Intro to Keras vs Pytorch

Keras was released in 2015 and designed to be as simple and easy to use as possible. Features of the Keras language include high-level APIs, an intuitive interface, and robust documentation. 

PyTorch was released in 2016 as a low-level API that’s designed to support machine learning and artificial intelligence applications within the scientific and mathematical space. PyTorch can be used for deep learning and large data sets, primarily numerical optimization tasks.

Related Article: PyTorch vs TensorFlow: Difference You Need To Know

PyTorch: A Low-Level Environment Designed for Power

PyTorch is an open-source machine learning framework designed for a low-level environment. Developed by Facebook and distributed under the BSD license, PyTorch can be used for free by anyone. 

As a deep learning solution, PyTorch can mill through, analyze, and identify large volumes of data. 

Scientists use PyTorch to create and train models, which can then be used to simulate processing and intelligence. As a low-level environment, PyTorch provides significant levels of flexibility and power, though it can be challenging for a beginner.

Pros: 

• PyTorch’s low-level environment is fast and efficient.
• Provides an extremely productive environment for developers and scientists.
• Easier to debug than TensorFlow, although somewhat comparable to Keras.
• Has an active (and youthful) community.

Cons: 

• PyTorch hasn’t yet experienced widespread adoption because it’s still a relatively new platform. 
• Due to the above, there are areas in which PyTorch documentation and the community may be lacking.
• There aren’t monitoring or visualization tools built into PyTorch.

Is PyTorch Better Than Keras?

For some applications, PyTorch is better. Mathematicians, statisticians, and scientists who are looking for deeper control into the mathematics and algorithms behind deep learning will find PyTorch to be the superior environment. 

In any situation in which a low-level environment is preferred, PyTorch will be better.

Is PyTorch Similar to Keras?

PyTorch and Keras are very similar. In fact, most deep learning or machine learning applications could be completed in either PyTorch or Keras with very similar end results. 

The difference really lies in how the systems are used. PyTorch provides more direct control over functions whereas Keras provides a sort of streamlined, high-level control. 

Why Do We Use PyTorch? 

PyTorch is used for applications such as natural language processing (NLP), video analysis, and image recognition. PyTorch is used in the Tesla AutoPilot system as well as Uber’s Pyro system. 

Keras: A High-Level Environment Designed for Ease of Use

Keras is a framework for interfacing with high-level APIs in machine learning backends such as TensorFlow. 

TensorFlow is a deep learning open-source end-to-end solution, which provides all the low-level and high-level APIs and utilities necessary to create, train, and deploy neural networks. 

With Keras, using TensorFlow (and other machine learning backends) becomes much easier. But Keras itself is not a machine learning library.

Pros:

• Simple and easy to use.
• Keras can be used with TensorFlow, Theano, Microsoft CNTK, and other backends.
• Includes pre-trained models, which can be used for faster deployment.
• Has robust documentation and an active community.
• Compared to TensorFlow, Keras provides superior debugging utilities.

Cons: 

• Can be inefficient and slow.
• Keras will not give you access to low-level computations and may error when you operate low-level APIs.
• Not all the debugging utilities or errors are useful.

Is Keras a Deep Learning Framework?

Keras would be more accurately called a deep learning API, which runs on top of a machine learning framework. 

Keras is designed to make deployment of machine learning applications much faster, by making it easier for developers to hit the ground running. 

Keras can be called a deep learning framework, but only with the understanding that the actual core libraries need to be called from elsewhere.

What Does the Keras Framework Run on?

Keras can run on TensorFlow and other similar open-source machine learning backends, such as Theanos. It is most popular on TensorFlow because it’s considered the official interface of TensorFlow. 

TensorFlow itself (as well as other machine learning backends) can be used separately without Keras, but Keras provides many benefits, the most important of which is accessibility.

Does Keras Require TensorFlow?

Keras does not require TensorFlow, but Keras does require some form of backend. Keras provides an interface and customization layer for an open-source machine learning backend, most frequently TensorFlow. 

Without a backend, Keras cannot provide deep learning capabilities; it is a framework, not a library.

The Differences Between PyTorch vs Keras

As you can see, the primary difference between PyTorch and Keras is how user-friendly they are. 

PyTorch exposes low-level operations and allows programmers to use the Python programming language to deeply customize their applications. 

Keras uses high-level operations to provide an easier but potentially more shallow experience.

Here’s a handy PyTorch Keras comparison chart:

As you can see, one of the major differences in PyTorch and Keras is that PyTorch is faster and can deal with larger data sets. Keras can be slow, which means that crunching larger data sets can be a challenge.

Most of the differences encountered between PyTorch and Keras simply have to do with the fact that Keras is a sophisticated, abstracted layer on top of TensorFlow, whereas PyTorch provides more direct algorithm-first modeling. At the same time, this means that PyTorch’s architecture is extremely complex compared to the one offered by Keras.

Whether one is easier for you to learn than the other depends largely on your background. If you have a scientific background, you might still find PyTorch simpler.

PyTorch vs Keras: Speed

PyTorch is faster than Keras. But PyTorch is about on par with TensorFlow. Because Keras provides an additional layer of abstraction between the user and TensorFlow, it will always be innately slower and less scalable. 

However, if a developer switches to TensorFlow rather than Keras, they can achieve speeds comparable to PyTorch.

PyTorch vs Keras: Usability

Both PyTorch and Keras are designed to be easier to debug than TensorFlow. 

Both PyTorch and Keras should be equally easy to use for an advanced, experienced developer. 

For those who are more scientifically-minded, PyTorch will be easier to use. PyTorch provides greater levels of visibility into mathematics and algorithms. 

For those who don’t come from a development background or who are new to machine learning, Keras will likely be easier to use. Keras streamlines and improves upon many of the functions of TensorFlow — but TensorFlow itself is not overtly challenging for experienced developers.

PyTorch vs Keras: Jobs

Keras/TensorFlow jobs pay on average $148,508 per year, according to ZipRecruiter. 

Comparatively, PyTorch jobs pay on average $100,800 per year on the same platform. 

Why do Keras jobs seem to pay significantly more than PyTorch jobs, if PyTorch is the more challenging technology?

Part of this may simply be because PyTorch is a newer technology. It is a younger framework, which means there’s less adoption and less awareness within the market. Both Keras and PyTorch developers are in demand in the market and will have many opportunities available.

What About PyTorch vs TensorFlow?

A comparison between PyTorch and TensorFlow is different from PyTorch vs Keras. Compared to PyTorch, TensorFlow is as fast as PyTorch, but lacks in debugging capabilities. Many of the disadvantages of Keras are stripped away from TensorFlow, but so are some of the advantages.

TensorFlow is similarly complex to PyTorch and will provide more depth and access to low-level utilities. At the same time, TensorFlow will be harder to learn. With Keras, ease of use and rapid deployment come at a cost — speed and efficiency.

Learning PyTorch or Keras

Whether you decide to learn PyTorch frameworks or the Keras machine learning suite, you should find them both to be robust, well-documented solutions. From PyTorch to Keras, applications for machine learning continue to grow. 

If you are a data scientist, mathematician, or analyst, PyTorch is likely the better framework. Likewise, if you’re doing a lot of custom programming and you need low-level, granular control, PyTorch can provide this.

But if you’re more interested in general management for neural networks and deep learning, Keras will be easier. When paired with TensorFlow, Keras can be an extremely robust deep learning framework, and can provide quality-of-life tools such as easier debugging through warnings and alerts.

Machine Learning Intro for Python Developers

Keras vs PyTorch: Head-to-Head Comparison

The Bottom Line

• PyTorch is better for scientists who need fast, scalable operations over large data sets.
• Keras is better for programmers who need easy, rapid operations over small data sets.

Either way, both PyTorch and Keras run on Python. Learning Python may be the first step if you’re interested in learning more about deep learning, artificial intelligence, and neural networks.

Learn Python Today!

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